Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However learning-based robot manipulation trained on a limited category within a simulator often struggles to achieve generalizability especially when confronted with extensive categories. Therefore we introduce an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation. By fine-tuning the injected adapters we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation. The fundamental insight lies in the introduced fine-tuning paradigm encompassing object category understanding affordance prior reasoning and object-centric pose prediction to stimulate the reasoning ability of MLLM in manipulation. During inference our approach utilizes an RGB image and text prompt to predict the end effector's pose in chain of thoughts. After the initial contact is established an active impedance adaptation policy is introduced to plan the upcoming waypoints in a closed-loop manner. Moreover in real world we design a test-time adaptation (TTA) strategy for manipulation to enable the model better adapt to the current real-world scene configuration. Experiments in simulator and real-world show the promising performance of ManipLLM. More details and demonstrations can be found at https://sites.google.com/view/manipllm.
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SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
PDF ↗Diffusion models have demonstrated strong potential for robotic trajectory planning. However generating coherent trajectories from high-level instructions remains challenging especially for long-range composition tasks requiring multiple sequential skills. We propose SkillDiffuser an end-to-end hierarchical planning framework integrating interpretable skill learning with conditional diffusion planning to address this problem. At the higher level the skill abstraction module learns discrete human-understandable skill representations from visual observations and language instructions. These learned skill embeddings are then used to condition the diffusion model to generate customized latent trajectories aligned with the skills. This allows generating diverse state trajectories that adhere to the learnable skills. By integrating skill learning with conditional trajectory generation SkillDiffuser produces coherent behavior following abstract instructions across diverse tasks. Experiments on multi-task robotic manipulation benchmarks like Meta-World and LOReL demonstrate state-of-the-art performance and human-interpretable skill representations from SkillDiffuser. More visualization results and information could be found on https://skilldiffuser.github.io/.
Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities with the majority focusing on the third-person perspective and only a few addressing specific tasks from the first-person perspective. However the capability of VLMs to "think" from a first-person perspective a crucial attribute for advancing autonomous agents and robotics remains largely unexplored. To bridge this research gap we introduce EgoThink a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs we evaluate twenty-one popular VLMs on EgoThink. Moreover given the open-ended format of the answers we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics.
The ability to associate touch with other modalities has huge implications for humans and computational systems. However multimodal learning with touch remains challenging due to the expensive data collection process and non-standardized sensor outputs. We introduce UniTouch a unified tactile model for vision-based touch sensors connected to multiple modalities including vision language and sound. We achieve this by aligning our UniTouch embeddings to pretrained image embeddings already associated with a variety of other modalities. We further propose learnable sensor-specific tokens allowing the model to learn from a set of heterogeneous tactile sensors all at the same time. UniTouch is capable of conducting various touch sensing tasks in the zero-shot setting from robot grasping prediction to touch image question answering. To the best of our knowledge UniTouch is the first to demonstrate such capabilities.
Conditional human motion generation is an important topic with many applications in virtual reality gaming and robotics. While prior works have focused on generating motion guided by text music or scenes these typically result in isolated motions confined to short durations. Instead we address the generation of long continuous sequences guided by a series of varying textual descriptions. In this context we introduce FlowMDM the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this we introduce the Blended Positional Encodings a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically global motion coherence is recovered at the absolute stage whereas smooth and realistic transitions are built at the relative stage. As a result we achieve state-of-the-art results in terms of accuracy realism and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention which makes it robust against varying text descriptions at inference time. Finally to address the limitations of existing HMC metrics we propose two new metrics: the Peak Jerk and the Area Under the Jerk to detect abrupt transitions.
Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors including vibration and structural stress when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles such as autonomous freight trucks and semi-trucks the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration and substantially improves downstream stereo depth post-rectification. We release two public datasets (https://light.princeton.edu/online-stereo-recification/) a synthetic and experimental wide baseline dataset to foster further research.
Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work we demonstrate that a critical and distinct challenge in 3D vision language reasoning is the situational awareness which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. (2) The agent answers open-ended questions from the perspective of its calculated position. To address this challenge we introduce SIG3D an end-to-end Situation-Grounded model for 3D vision language reasoning. We tokenize the 3D scene into sparse voxel representation and propose a language-grounded situation estimator followed by a situated question answering module. Experiments on the SQA3D and ScanQA datasets show that SIG3D outperforms state-of-the-art models in situational estimation and question answering by a large margin (e.g. an enhancement of over 30% on situation accuracy). Subsequent analysis corroborates our architectural design choices explores the distinct functions of visual and textual tokens and highlights the importance of situational awareness in the domain of 3D question-answering. Project page is available at https://yunzeman.github.io/situation3d.
We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen objects using the grasp distribution learned from the training set they often exhibit a significant performance drop when encountering objects with diverse shapes and structures. To enhance the grasp detection methods' generalization ability we incorporate domain prior knowledge of robotic grasping enabling better adaptation to objects with significant shape and structure differences. More specifically we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects we design a contact-score joint optimization using the projection contact map to refine these poses in cluttered scenarios. Extensive experiments conducted on the GraspNet-1billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our generalizing 6-DoF grasp detection method.
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
PDF ↗The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering. Segment-Anything (SAM) among others is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot generalization. Despite the success recent studies reveal the weakness of SAM under strong distribution shift. In particular SAM performs awkwardly on corrupted natural images camouflaged images medical images etc. Motivated by the observations we aim to develop a self-training based strategy to adapt SAM to target distribution. Given the unique challenges of large source dataset high computation cost and incorrect pseudo label we propose a weakly supervised self-training architecture with anchor regularization and low-rank finetuning to improve the robustness and computation efficiency of adaptation. We validate the effectiveness on 5 types of downstream segmentation tasks including natural clean/corrupted images medical images camouflaged images and robotic images. Our proposed method is task-agnostic in nature and outperforms pre-trained SAM and state-of-the-art domain adaptation methods on almost all downstream tasks with the same testing prompt inputs.
Image-based mirror detection has recently undergone rapid research due to its significance in applications such as robotic navigation semantic segmentation and scene reconstruction. Recently VMD-Net was proposed as the first video mirror detection technique by modeling dual correspondences between the inside and outside of the mirror both spatially and temporally. However this approach is not reliable as correspondences can occur completely inside or outside of the mirrors. In addition the proposed dataset VMD-D contains many small mirrors limiting its applicability to real-world scenarios. To address these problems we developed a more challenging dataset that includes mirrors of various shapes and sizes at different locations of the frames providing a better reflection of real-world scenarios. Next we observed that the motions between the inside and outside of the mirror are often inconsistent. For instance when moving in front of a mirror the motion inside the mirror is often much smaller than the motion outside due to increased depth perception. With these observations we propose modeling inconsistent motion cues to detect mirrors and a new network with two novel modules. The Motion Attention Module (MAM) explicitly models inconsistent motions around mirrors via optical flow and the Motion-Guided Edge Detection Module (MEDM) uses motions to guide mirror edge feature learning. Experimental results on our proposed dataset show that our method outperforms state-of-the-arts. The code and dataset are available at https://github.com/AlexAnthonyWarren/MG-VMD.
Existing approaches to video understanding mainly designed for short videos from a third-person perspective are limited in their applicability in certain fields such as robotics. In this paper we delve into open-ended question-answering (QA) in long egocentric videos which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges including the complexity of temporally grounding queries within extensive video content the high resource demands for precise data annotation and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation; (ii) employing large language models for efficient and scalable data synthesis; and (iii) introducing a close-ended QA task for evaluation to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code data and models are open-sourced at https://github.com/Becomebright/GroundVQA.
UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence
PDF ↗Garment manipulation (e.g. unfolding folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks while highly challenging due to the diversity of garment configurations geometries and deformations. Although able to manipulate similar shaped garments in a certain task previous works mostly have to design different policies for different tasks could not generalize to garments with diverse geometries and often rely heavily on human-annotated data. In this paper we leverage the property that garments in a certain category have similar structures and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios using one or two arms taking one or more steps inputting flat or messy garments demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip.
Trajectory prediction plays an important role in various applications including autonomous driving robotics and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets typically employing a standardized input duration. However a notable issue arises when these models are evaluated with varying observation lengths leading to a significant performance drop a phenomenon we term the Observation Length Shift. To address this issue we introduce a general and effective framework the FlexiLength Network (FLN) to enhance the robustness of existing trajectory prediction techniques against varying observation periods. Specifically FLN integrates trajectory data with diverse observation lengths incorporates FlexiLength Calibration (FLC) to acquire temporal invariant representations and employs FlexiLength Adaptation (FLA) to further refine these representations for more accurate future trajectory predictions. Comprehensive experiments on multiple datasets i.e. ETH/UCY nuScenes and Argoverse 1 demonstrate the effectiveness and flexibility of our proposed FLN framework.
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
PDF ↗Normalizing flows have proven their efficacy for density estimation in Euclidean space but their application to rotational representations crucial in various domains such as robotics or human pose modeling remains underexplored. Probabilistic models of the human pose can benefit from approaches that rigorously consider the rotational nature of human joints. For this purpose we introduce HuProSO3 a normalizing flow model that operates on a high-dimensional product space of SO(3) manifolds modeling the joint distribution for human joints with three degrees of freedom. HuProSO3's advantage over state-of-the-art approaches is demonstrated through its superior modeling accuracy in three different applications and its capability to evaluate the exact likelihood. This work not only addresses the technical challenge of learning densities on SO(3) manifolds but it also has broader implications for domains where the probabilistic regression of correlated 3D rotations is of importance. Code will be available at https://github.com/odunkel/HuProSO.
Realizing unified monocular 3D object detection including both indoor and outdoor scenes holds great importance in applications like robot navigation. However involving various scenarios of data to train models poses challenges due to their significantly different characteristics e.g. diverse geometry properties and heterogeneous domain distributions. To address these challenges we build a detector based on the bird's-eye-view (BEV) detection paradigm where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors. Then we split the classical BEV detection architecture into two stages and propose an uneven BEV grid design to handle the convergence instability caused by the aforementioned challenges. Moreover we develop a sparse BEV feature projection strategy to reduce computational cost and a unified domain alignment method to handle heterogeneous domains. Combining these techniques a unified detector UniMODE is derived which surpasses the previous state-of-the-art on the challenging Omni3D dataset (a large-scale dataset including both indoor and outdoor scenes) by 4.9% \rm AP_ 3D revealing the first successful generalization of a BEV detector to unified 3D object detection.
This paper introduces Hierarchical Diffusion Policy (HDP) a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP) and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-level actions. To generate context-aware motion trajectories while satisfying robot kinematics constraints we present a novel kinematics-aware goal-conditioned control agent Robot Kinematics Diffuser (RK-Diffuser). Specifically RK-Diffuser learns to generate both the end-effector pose and joint position trajectories and distill the accurate but kinematics-unaware end-effector pose diffuser to the kinematics-aware but less accurate joint position diffuser via differentiable kinematics. Empirically we show that HDP achieves a significantly higher success rate than the state-of-the-art methods in both simulation and real-world.
The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However this distillation process involves finding a correct mode in the high-dimensional and large-variance distribution produced by the diffusion model. This task is challenging and often leads to issues such as over-saturation over-smoothing and Janus-like artifacts in the 3D generation. In this paper we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner which unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt. Moreover by harnessing the latent space of GANs and expressive diffusion model priors our method enables a wide variety of 3D applications including single-view reconstruction high diversity generation and continuous 3D interpolation in open domain. Our experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.
Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic human-robot-mixed environments. Context information such as road maps and surrounding agents' states provides crucial geometric and semantic information for motion behavior prediction. To this end recent works explore two-stage prediction frameworks where coarse trajectories are first proposed and then used to select critical context information for trajectory refinement. However they either incur a large amount of computation or bring limited improvement if not both. In this paper we introduce a novel scenario-adaptive refinement strategy named SmartRefine to refine prediction with minimal additional computation. Specifically SmartRefine can comprehensively adapt refinement configurations based on each scenario's properties and smartly chooses the number of refinement iterations by introducing a quality score to measure the prediction quality and remaining refinement potential of each scenario. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. Experiments on Argoverse (1 & 2) show that our method consistently improves the prediction accuracy of multiple state-of-the-art prediction models. Specifically by adding SmartRefine to QCNet we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission. Comprehensive studies are also conducted to ablate design choices and explore the mechanism behind multi-iteration refinement. Codes are available at https://github.com/opendilab/SmartRefine/.
Grasp detection is a persistent and intricate challenge with various industrial applications. Recently many methods and datasets have been proposed to tackle the grasp detection problem. However most of them do not consider using natural language as a condition to detect the grasp poses. In this paper we introduce Grasp-Anything++ a new language-driven grasp detection dataset featuring 1M samples over 3M objects and upwards of 10M grasping instructions. We utilize foundation models to create a large-scale scene corpus with corresponding images and grasp prompts. We approach the language-driven grasp detection task as a conditional generation problem. Drawing on the success of diffusion models in generative tasks and given that language plays a vital role in this task we propose a new language-driven grasp detection method based on diffusion models. Our key contribution is the contrastive training objective which explicitly contributes to the denoising process to detect the grasp pose given the language instructions. We illustrate that our approach is theoretically supportive. The intensive experiments show that our method outperforms state-of-the-art approaches and allows real-world robotic grasping. Finally we demonstrate our large-scale dataset enables zero-short grasp detection and is a challenging benchmark for future work.
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
PDF ↗Various pose estimation and tracking problems in robotics can be decomposed into a correspondence estimation problem (often computed using a deep network) followed by a weighted least squares optimization problem to solve for the poses. Recent work has shown that coupling the two problems by iteratively refining one conditioned on the other's output yields SOTA results across domains. However training these models has proved challenging requiring a litany of tricks to stabilize and speed up training. In this work we take the visual odometry problem as an example and identify three plausible causes: (1) flow loss interference (2) linearization errors in the bundle adjustment (BA) layer and (3) dependence of weight gradients on the BA residual. We show how these issues result in noisy and higher variance gradients potentially leading to a slow down in training and instabilities. We then propose a simple solution to reduce the gradient variance by using the weights predicted by the network in the inner optimization loop to also weight the correspondence objective in the training problem. This helps the training objective 'focus' on the more important points thereby reducing the variance and mitigating the influence of outliers. We show that the resulting method leads to faster training and can be more flexibly trained in varying training setups without sacrificing performance. In particular we show 2-2.5x training speedups over a baseline visual odometry model we modify.