Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation (2011.06719v1)

Abstract: Billions of people use chopsticks, a simple yet versatile tool, for fine manipulation of everyday objects. The small, curved, and slippery tips of chopsticks pose a challenge for picking up small objects, making them a suitably complex test case. This paper leverages human demonstrations to develop an autonomous chopsticks-equipped robotic manipulator. Due to the lack of accurate models for fine manipulation, we explore model-free imitation learning, which traditionally suffers from the covariate shift phenomenon that causes poor generalization. We propose two approaches to reduce covariate shift, neither of which requires access to an interactive expert or a model, unlike previous approaches. First, we alleviate single-step prediction errors by applying an invariant operator to increase the data support at critical steps for grasping. Second, we generate synthetic corrective labels by adding bounded noise and combining parametric and non-parametric methods to prevent error accumulation. We demonstrate our methods on a real chopstick-equipped robot that we built, and observe the agent's success rate increase from 37.3% to 80%, which is comparable to the human expert performance of 82.6%.

• The paper introduces novel methods—object-centric data transformation and noise injection—to effectively combat covariate shift in imitation learning.
• It leverages synthetic corrective labels and invariant transformation to enhance policy generalization, elevating manipulation success rates significantly.
• The study underscores scalable model-free learning approaches for fine robotic manipulation without the need for interactive expert input.

Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation

Introduction

The paper "Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation" investigates the autonomous manipulation of objects using chopsticks, demonstrating its potential as a complex manipulation task within the field of robotics. The authors focus on developing a robotic system capable of such dexterous manipulation employing model-free imitation learning techniques while addressing the inherent challenges of covariate shift—a phenomenon that often leads to poor policy generalization in supervised learning setups. The paper is motivated by the difficulty of constructing precise models for tasks involving fine manipulation, thus necessitating the utilization of model-free approaches.

Methodology

The paper proposes two primary methodologies to mitigate covariate shift without the necessity for an interactive expert or an environmental model:

1. Increasing Data Support through Invariant Transformation: By transforming the data into an object-centric frame, the authors aim to densify the data distribution around critical regions that determine grasp success. This transformation is posited to reduce covariate shift and enhance policy generalization.
2. Synthetic Corrective Labels via Noise Injection: The second approach generates corrective labels by injecting bounded noise into the collected state and reusing the resultant action labels. This method assumes action-label smoothness and seeks to implicitly guide the policy towards recovery from deviated states. The paper underscores the synthesis of parametric (e.g., neural networks) and non-parametric methods (e.g., k-nearest neighbors) to improve action distribution matching and mitigate error accumulation.

The proposed methodologies were evaluated using a custom-built robotic system equipped with chopsticks, which demonstrated a significant improvement in success rates from 37.3% to 80% following the interventions, aligning closely with human expert performance benchmarks.

Results and Implications

The experimental results underscore the efficacy of the introduced methods: transforming data to an object-centric frame and noise-injecting synthetic labels effectively addressed the covariate shift, as evidenced by higher success rates and a greater alignment of state distributions during testing. Additionally, the scalability of the framework was validated across several tasks in simulated MuJoCo environments, supporting the generality of the proposed noise injection technique.

The paper's contributions have significant implications for robotic fine manipulation, particularly in environments where constructing precise models is infeasible, or interactive expert access is constrained. The methods demonstrated here can be directly extended to interactive setups, offering potential improvements in robustness and reduced user involvement.

Future Directions

While the paper provides compelling evidence for model-free imitation learning and novel approaches to covariate shift, it opens avenues for further research. Future work could explore alternative noise injection strategies, leveraging an inaccurate model to complement demonstration data, or refining distance functions in non-parametric methods to enhance smoothness. The integration of model-based methods with the proposed approaches could lead to sophisticated systems capable of tackling broader object manipulation tasks with high dexterity.

In sum, this paper advances our understanding of imitation learning applications in complex manipulation tasks, presenting innovative methodologies to enhance performance despite the absence of interactive experts or detailed models. Its results contribute to the ongoing dialogue on efficient robotic learning strategies and underscore the importance of addressing covariate shifts in imitation learning frameworks.