State-Only Imitation Learning for Dexterous Manipulation (2004.04650v2)

Abstract: Modern model-free reinforcement learning methods have recently demonstrated impressive results on a number of problems. However, complex domains like dexterous manipulation remain a challenge due to the high sample complexity. To address this, current approaches employ expert demonstrations in the form of state-action pairs, which are difficult to obtain for real-world settings such as learning from videos. In this paper, we move toward a more realistic setting and explore state-only imitation learning. To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations. The inverse dynamics model and the policy are trained jointly. Our method performs on par with state-action approaches and considerably outperforms RL alone. By not relying on expert actions, we are able to learn from demonstrations with different dynamics, morphologies, and objects. Videos available at https://people.eecs.berkeley.edu/~ilija/soil .

• The paper presents SOIL, a framework that overcomes high sample complexity in dexterous manipulation by learning exclusively from state sequences.
• SOIL jointly trains a policy and an inverse dynamics model to predict missing actions from sequential states, enabling effective imitation without explicit action data.
• Results show that SOIL performs on par with traditional state-action methods while offering a robust, real-world solution for robotic dexterity.

Overview of State-Only Imitation Learning for Dexterous Manipulation

The paper "State-Only Imitation Learning for Dexterous Manipulation" presents a novel approach to imitation learning aimed at addressing the high sample complexity challenge in dexterous manipulation tasks using multifingered robotic hands. The inherent difficulty in these tasks arises from complex high-dimensional action spaces that make standard reinforcement learning (RL) approaches inefficient and demanding. Traditional methods that utilize state-action pairs face difficulties, especially regarding data collection and adaptation to new settings such as learning from internet videos. The proposed method, SOIL (State-Only Imitation Learning), endeavors to leverage demonstrations without reliance on explicit action data, thereby broadening the applicability and efficiency of imitation learning.

Methodology

SOIL is grounded in a more pragmatic imitation learning setup where demonstrations provide only state information. This setting is more aligned with real-world scenarios, where acquiring exact action sequences from human demonstrators, as might be captured in online videos, is not feasible.

1. Inverse Dynamics Model: At the core of SOIL is an inverse dynamics model trained to predict actions based on two consecutive states. This model is iteratively trained using trajectories emanating from the policy's exploration, serving as a mechanism to simulate the missing action data in state-only demonstrations.
2. Joint Training of Policy and Inverse Model: The approach involves the joint optimization of the policy network and the inverse dynamics model, allowing the policy to benefit from self-supervised learning of inverse dynamics. The policy gradient optimization is augmented with action predictions derived from state-only demonstration data, helping the agent initially navigate the action space effectively, and migrate toward RL-driven exploration later.
3. Algorithm Execution: The training procedure iteratively collects interaction data in a replay buffer to inform the inverse dynamics model and updates the policy both with actual rewards and imitation learning objectives derived from predicted actions on demonstration states.

Results and Implications

The simulation results on dexterous manipulation tasks such as object relocation, in-hand manipulation, door opening, and tool use reveal that SOIL achieves performance comparable to methods relying on state-action pairs and significantly outperforms standard RL frameworks without demonstration assistance. This parity with state-action approaches indicates that access to action data might not be crucial for imitation learning, suggesting broader applicability when demonstration mismatch occurs—such as differences in dynamics, morphology, or objects.

The qualitative analysis also supports claims of realistic and effective policy learning, demonstrating the ability to achieve proficient manipulation skills through state-only strategies.

Future Directions

The implications of this paper are substantial. By minimizing dependence on action data, SOIL simplifies the data collection process and provides avenues for learning from diverse settings, thus bridging the gap toward utilizing internet-scale human video demonstrations without precise action annotation. Future research could extend these findings to real-world robotic systems and examine the integration of perceived state data from video sources as a precursor to more generalized models suited for heterogeneous task environments.

Overall, SOIL presents a pivotal methodology in the domain of imitation learning for dexterous manipulation, promising improvements in sample efficiency, adaptability, and practical deployment of robotic dexterity akin to human capabilities in dynamic interaction scenarios.