DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References (2502.09614v1)

Abstract: We address the challenge of developing a generalizable neural tracking controller for dexterous manipulation from human references. This controller aims to manage a dexterous robot hand to manipulate diverse objects for various purposes defined by kinematic human-object interactions. Developing such a controller is complicated by the intricate contact dynamics of dexterous manipulation and the need for adaptivity, generalizability, and robustness. Current reinforcement learning and trajectory optimization methods often fall short due to their dependence on task-specific rewards or precise system models. We introduce an approach that curates large-scale successful robot tracking demonstrations, comprising pairs of human references and robot actions, to train a neural controller. Utilizing a data flywheel, we iteratively enhance the controller's performance, as well as the number and quality of successful tracking demonstrations. We exploit available tracking demonstrations and carefully integrate reinforcement learning and imitation learning to boost the controller's performance in dynamic environments. At the same time, to obtain high-quality tracking demonstrations, we individually optimize per-trajectory tracking by leveraging the learned tracking controller in a homotopy optimization method. The homotopy optimization, mimicking chain-of-thought, aids in solving challenging trajectory tracking problems to increase demonstration diversity. We showcase our success by training a generalizable neural controller and evaluating it in both simulation and real world. Our method achieves over a 10% improvement in success rates compared to leading baselines. The project website with animated results is available at https://meowuu7.github.io/DexTrack/.

• The paper introduces a neural tracking controller that fuses reinforcement and imitation learning to achieve over 10% performance improvements in dexterous tasks.
• It employs a novel data flywheel and homotopy optimization scheme to retarget human demonstrative trajectories to robotic actions.
• Comprehensive experiments on GRAB and TACO datasets confirm the method's robustness in both simulated and real-world environments.

Overview of "DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References"

The paper presents DexTrack, a comprehensive framework aimed at developing a generalizable neural tracking controller for dexterous manipulation by leveraging human references. This paper addresses several technical challenges predominantly associated with dexterous robotic manipulation, which involves intricate dynamics and the need for a controller that is not only robust but also adaptable to various object interactions.

Key Contributions

1. Neural Tracking Controller Design: At the core of the presented work is a neural tracking controller, developed to manage dexterous robot manipulation guided by human demonstrations. This controller is designed to mitigate limitations of prior methods, which depend on task-specific rewards or precise system models, by integrating reinforcement learning (RL) and imitation learning (IL) to enhance controller performance in dynamic and unpredictable environments.
2. Data Utilization: The paper introduces a strategic data-driven approach that leverages a broad array of high-quality robot tracking demonstrations. These demonstrations consist of paired data of kinematic human references and corresponding robot actions, iteratively improved through a so-called data flywheel and homotopy optimization scheme. This method is akin to a chain-of-thought approach, optimizing complex trajectory tracking by simplifying reference motions through a homotopy path.
3. Performance Enhancements: Utilizing a mix of RL and IL, DexTrack effectively guides the development of a controller that surpasses existing approaches, achieving over a 10% improvement in success rates on challenging manipulation tracking tasks both in simulated environments and real-world scenarios.

Detailed Methodology

DexTrack employs a multi-stage method:

• Data Preparation: Initial processing involves retargeting human hand-object manipulation trajectories to the robot's framework, creating a set of reference motions for training.
• Reinforcement and Imitation Learning Integration: This hybrid learning approach enhances the neural controller's performance by combining the exploration capabilities of RL with the guided supervision of IL, based on robust demonstrations.
• Homotopy Optimization Scheme: This introduces a per-trajectory tracking approach, optimizing individual trajectories iteratively. The scheme incrementally simplifies a trajectory into a series of tasks to improve tracking accuracy and is crucial for diversifying the demonstration dataset.

Experimental Validation

Comprehensive experiments demonstrate the robustness and generalization capabilities of the DexTrack system for various tool-use and hand-object interactions:

• Evaluations were conducted using two datasets: GRAB, which features daily interactions, and TACO, showcasing functional interactions.
• Quantitative measures, such as object translation and rotation errors, clearly show DexTrack's superior performance compared to current baseline methods.
• Real-world experiments further substantiated the transferability of the controller, where it excelled in adapting to real-time scenarios despite challenges like novel object shapes and noise in kinematic references.

Implications and Future Directions

DexTrack's sophisticated use of large-scale high-quality demonstrations and innovative homotopy optimization sets a new standard in robotic dexterous manipulation. The framework's proven capacity to handle complex, dynamic tasks suggests promising future applications in robotics, broadening the spectrum of real-world tasks that robotic systems can autonomously manage.

Future developments could include refining the homotopy optimization scheme to reduce computation costs, potentially making the training workflow more time-efficient. Additionally, extending the adaptation and robustness of the system to a broader range of tools and objects remains a compelling area for further research.

In conclusion, the paper presented in DexTrack provides significant advances in creating adaptable and robust neural tracking systems for dexterous robotic manipulation, paving the way for innovative applications in human-comparable dexterity in robotics.