Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners? (2412.09743v3)

Abstract: The tremendous success of behavior cloning (BC) in robotic manipulation has been largely confined to tasks where demonstrations can be effectively collected through human teleoperation. However, demonstrations for contact-rich manipulation tasks that require complex coordination of multiple contacts are difficult to collect due to the limitations of current teleoperation interfaces. We investigate how to leverage model-based planning and optimization to generate training data for contact-rich dexterous manipulation tasks. Our analysis reveals that popular sampling-based planners like rapidly exploring random tree (RRT), while efficient for motion planning, produce demonstrations with unfavorably high entropy. This motivates modifications to our data generation pipeline that prioritizes demonstration consistency while maintaining solution diversity. Combined with a diffusion-based goal-conditioned BC approach, our method enables effective policy learning and zero-shot transfer to hardware for two challenging contact-rich manipulation tasks.

• The paper introduces a refined data generation pipeline that produces low-entropy demonstrations using smoothed contact dynamics and high-fidelity simulation.
• It applies goal-conditioned behavior cloning with diffusion models and hindsight goal relabeling to effectively capture multi-modal policy learning.
• Empirical results on dexterous in-hand and bimanual tasks demonstrate improved success rates and reduced action entropy compared to traditional RRT approaches.

Learning Contact-Rich Manipulation Policies from Model-Based Planners

This paper investigates the feasibility and methodology for learning contact-rich manipulation policies for robotic systems using data generated from model-based planners. The work specifically addresses the challenges in generating effective demonstrations for intricate tasks that involve complex coordination among multiple contact points, which are known to be difficult to achieve through human teleoperation due to the limitations of current interfaces.

The authors critique the existing practice of using rapidly exploring random tree (RRT), a popular sampling-based planner for motion planning, highlighting its production of demonstrations with high entropy. The paper posits that such high entropy contributes to difficulty in learning when employing behavior cloning (BC). This motivated the development of a refined demonstration generation pipeline focusing on consistency while keeping solution diversity intact.

Key contributions of the paper include:

1. Data Generation Pipeline: An innovative approach is proposed for synthesizing training data. Using smoothed contact dynamics for planning and then executing the planned trajectory in a high-fidelity physics simulator ensures actionable and precise demonstrations of contact-rich tasks. This is crucial, as the transition from theoretical plans to physical enactments often involves discrepancies, especially concerning contact dynamics.
2. Goal-Conditioned Behavior Cloning: The research capitalizes on goal-conditioned imitation learning (GCIL), integrating diffusion models to enhance policy learning. This is particularly useful for capturing multi-modality and optimizing policies using sub-optimal data. Hindsight goal relabeling allows for leveraging training data effectively by reinterpreting goal states that align with achieved states during task execution.
3. Empirical Evaluation: Two challenging tasks were assessed: in-hand object re-orientation using a dexterous robotic hand (AllegroHand) and bimanual manipulation of oversized objects (IiwaBimanual). With the AllegroHand, the framework was tested with both easy and hard variations, demonstrating its adaptability to different problem complexities. The IiwaBimanual task underscored the effectiveness of the greedy planner over traditional RRT by showing improved task success rates and lower entropy in action predictions.

The authors emphasize the necessity for low entropy in training data for effective policy learning, detailing that high-entropy actions result in a distribution that is challenging to capture accurately in a low-data regime. The use of a greedy planner for demonstration generation is shown to consistently outperform the RRT method by fostering a less stochastic action space.

Implications and Future Directions

The paper signals a shift from data collection reliant on human operators to synthetic data generation using model-based approaches, particularly for complex, contact-rich manipulations. This paradigm shift offers the potential for highly autonomous systems in industrial and service robotics.

However, the findings also underline the nuanced balance between state coverage and demonstration consistency, signaling an area ripe for further research. While planners like RRT ensure robust state-space exploration, the completeness has to be tempered with consistency for effective policy development.

Future explorations might delve into hybrid models that combine aspects of BC from model-generated and real-world data and incorporate dynamics-adaptive elements that react to system discrepancies between planner-based simulations and real-world enactments.

Overall, this work presents a significant advancement in robotic manipulation by adapting generative modeling frameworks to efficiently learn from model-based planning, establishing a groundwork for subsequent improvements in both data generation pipelines and planning algorithms in robotic learning.