From Simple to Complex Skills: The Case of In-Hand Object Reorientation (2501.05439v1)

Abstract: Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each new task requires substantial human effort, such as careful reward engineering, hyperparameter tuning, and system identification. In this work, we present a system that leverages low-level skills to address these challenges for more complex tasks. Specifically, we introduce a hierarchical policy for in-hand object reorientation based on previously acquired rotation skills. This hierarchical policy learns to select which low-level skill to execute based on feedback from both the environment and the low-level skill policies themselves. Compared to learning from scratch, the hierarchical policy is more robust to out-of-distribution changes and transfers easily from simulation to real-world environments. Additionally, we propose a generalizable object pose estimator that uses proprioceptive information, low-level skill predictions, and control errors as inputs to estimate the object pose over time. We demonstrate that our system can reorient objects, including symmetrical and textureless ones, to a desired pose.

• The paper presents a hierarchical policy that builds on pre-trained in-hand rotation skills to narrow the exploration space for object reorientation.
• It demonstrates eight times faster training convergence and robust performance across various object shapes and textures, validated in both simulation and real-world tests.
• By integrating a proprioceptive state estimator for accurate pose prediction, the method minimizes reliance on complex reward engineering and hyperparameter tuning.

In-Hand Object Reorientation through Hierarchical Policy and Pre-trained Skills

The paper "From Simple to Complex Skills: The Case of In-Hand Object Reorientation" presents a novel methodology to tackle challenges in dexterous manipulation, specifically focusing on in-hand object reorientation by leveraging pre-trained skills and hierarchical policies. This research can overcome significant barriers in robotic manipulation, such as the extensive human effort needed for sim-to-real transfer, by reducing the dependency on reward engineering and hyperparameter tuning.

Summary of the Approach

The core idea of this work is to employ a hierarchical policy structure to improve the robustness and efficiency of learning in-hand object reorientation tasks. This methodology is grounded on the incorporation of pre-trained low-level skills—specifically, single-axis rotation skills—and building a higher-level planner policy that decides when and how to employ these skills. This hierarchical policy system efficiently narrows the solution space and enhances training stability, enabling a smooth transition of skills from simulation to real-world scenarios.

Hierarchical Policy Design

• Low-level Skill Policy: The paper utilizes pre-trained in-hand object rotation policies, which significantly reduce the exploration space needed for learning new tasks by acting as foundational skills.
• High-level Planner Policy: This policy orchestrates the low-level skills based on feedback from both the environment and the low-level skill policies. It outputs commands for rotation axes and residual actions to refine and complement the actions of low-level skills.
• Residual Actions: These are deployed to allow additional error correction and adaptability, compensating for the limitations of fixed pre-trained skills.

Numerical Performance and Robustness

This framework demonstrated notable success in several metrics, indicating improvements over traditional approaches where skills are learned from scratch:

• Training Efficiency: The hierarchical policy converged eight times faster than a baseline method trained entirely from scratch.
• Stability and Robustness: The policy maintained high success rates, even under significant variability in object shapes, textures, and physical properties, suggesting strong generalization capabilities.
• Sim-to-Real Transfer: Real-world experiments confirmed the applicability and reliability of the proposed method, successfully manipulating various objects, some of which were significantly different from the training set.

State Estimation for Real-World Application

To enable the hierarchical policy's deployment in physical environments, a novel proprioceptive state estimator was introduced. This estimator integrates sensory input, low-level skill feedback, and prior actions to predict the object's pose over time. The design choice of decoupling state estimation from policy control allows for generalization across different object types without the need for retraining for each specific item. Robustness in both the prediction accuracy and the policy's tolerance to those predictions was confirmed through experimental validations.

Implications and Future Work

The implications of these contributions are far-reaching within the field of AI and robotics. Practically, such hierarchical policies and pre-trained skill utilizations can herald a new era of robotics where complex manipulation tasks are performed with less computational cost and increased reliability. Theoretically, this research provides insights into how humans learn complex tasks through building upon pre-existing skills, offering a blueprint for designing more sophisticated artificial learning systems.

Despite its success, the method relies heavily on the robustness of underlying low-level skills and could falter under excessive object slippage—a point identified for potential enhancement through tactile feedback integration. Future developments could focus on incorporating multi-modal sensory inputs, particularly touching and vision sensors, to refine object tracking and manipulation precision further.

Overall, this paper lays a substantial foundation for advancing robot dexterity through structured skill reuse and hierarchical control strategies—it presents a convincing step forward towards autonomous systems capable of nuanced task execution within realistic and unpredictable environments.