Hand-Object Interaction Pretraining from Videos (2409.08273v1)

Abstract: We present an approach to learn general robot manipulation priors from 3D hand-object interaction trajectories. We build a framework to use in-the-wild videos to generate sensorimotor robot trajectories. We do so by lifting both the human hand and the manipulated object in a shared 3D space and retargeting human motions to robot actions. Generative modeling on this data gives us a task-agnostic base policy. This policy captures a general yet flexible manipulation prior. We empirically demonstrate that finetuning this policy, with both reinforcement learning (RL) and behavior cloning (BC), enables sample-efficient adaptation to downstream tasks and simultaneously improves robustness and generalizability compared to prior approaches. Qualitative experiments are available at: \url{https://hgaurav2k.github.io/hop/}.

Hand-Object Interaction Pretraining from Videos

The paper "Hand-Object Interaction Pretraining from Videos" by Singh et al. addresses a significant challenge in robotic manipulation: deriving general manipulation priors from in-the-wild videos to generate sensorimotor trajectories for robots. By embedding both the human hand and manipulated objects in a shared 3D space and subsequently retargeting these human motions to robot actions, the authors develop a task-agnostic base policy using generative modeling. This policy significantly enhances sample efficiency, generalization, and robustness in robotic manipulation.

Key Insights and Contributions

1. General Manipulation Prior: The authors propose a novel approach to capture a general manipulation prior from videos. This prior is encoded in the weights of a causal transformer, pretrained with a conditional distribution matching objective on sensorimotor robot trajectories generated via a physically grounded simulator. This technique aligns with the current trends in vision and language research, leveraging the increasing quality and diversity of data to create more expressive models.
2. 3D Hand-Object Representation: By lifting human hand and object interactions into a 3D space, the authors can leverage simulations to map these interactions to robot actions. This method reintroduces the physics lost in video data back into the interactions, allowing for safer and more diverse training environments.
3. Simulator-in-the-Loop Retargeting: The paper introduces a robust method for retargeting human motion to robot actions by optimizing a cost function that minimizes the difference between human and robot keypoints. The authors emphasize the importance of simulation diversity, which is achieved by varying the environment setup to increase the overall richness of the extracted joint trajectories.
4. Empirical Validation: The empirical results are compelling. Pretraining on hand-object interactions significantly speeds up skill acquisition, improves generalization, and enhances robustness compared to prior methods. Finetuning the pretrained agents with reinforcement learning or behavioral cloning demonstrates considerable improvements in sample efficiency and task success rates.

Methodological Details

The core methodology comprises several critical steps:

• 3D Trajectory Extraction: Utilizing recent advances in 3D vision (specifically MCC-HO), the authors extract hand-object interaction trajectories from videos. The approach adapts to the inherent ambiguities of monocular videos by leveraging the human hand as an anchor.
• Mapping to Robot Embodiment: The extracted 3D trajectories are mapped to robot actions using a non-linear optimization process performed within a high-fidelity simulator. This mapping considers constraints like smoothness and collision avoidance to ensure realistic and hazardous-free robot actions.
• Generative Pretraining: The deep pretraining involves a generative modeling approach using transformers. The authors pretrain their policy on next-action prediction tasks given a sequence of sensory observations. This pretraining shapes the base policy with a broader understanding of manipulation dynamics, which is then fine-tuned for specific tasks.

Experimental Setup

The extensive experimental setup involves both real-world and simulation environments, emphasizing the transferability and adaptability of the learned priors:

• Real-World Experiments: Conducted on a 7-DoF xArm robot with a 16-DoF Allegro hand, tasks include "Grasp and Drop", "Grasp and Pour", and "Grasp and Lift". The results highlight the superior performance of the proposed approach, especially in complex tasks involving multiple objects.
• Simulation Experiments: Tasks like "Grasp and Lift", "Lift and Throw", and "Open Cabinet" are used to benchmark against traditional reinforcement learning baselines and demonstration-guided strategies. The proposed method showed a significant margin in sample efficiency and robustness.

Implications and Future Directions

The implications of this work are substantial for the field of robotic manipulation. The ability to derive a general manipulation prior from unstructured human-object interaction videos opens avenues for more adaptable and efficient robot learning paradigms. Practically, this can lead to more versatile robot systems capable of performing a wider range of tasks with fewer task-specific demonstrations.

Theoretically, this approach aligns with the trends of leveraging large-scale, unstructured data for pretraining, seen in vision and LLMs. It paves the way for future research to explore more complex scene reconstructions, potentially incorporating multi-object interactions and advanced physics. Improvements in 3D reconstruction techniques will likely enhance the quality and utility of the interaction data, making these models even more robust and generalizable.

Conclusion

Singh et al.'s work presents a significant step forward in leveraging human video data for robotic manipulation. Their approach to capturing and utilizing hand-object interaction priors provides a robust, flexible method for developing manipulation policies. This paper also brings attention to the potential of simulation-in-the-loop retargeting as a means to bridge the gap between human actions and robotic embodiments, significantly contributing to the broader goal of creating more intelligent and adaptable robotic systems.