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

Liyiming Ke, Jingqiang Wang+3 more|arXiv (Cornell University)|Nov 13, 2020

Robot Manipulation and Learning31 references4 citations

TL;DR

This paper proposes a non-interactive, model-free imitation learning approach to reduce covariate shift in fine manipulation using chopsticks, leveraging data augmentation via noise injection and object-centric state transformation to improve policy generalization. The method increases success rates from 37.3% to 80%, approaching expert performance of 82.6% on a real robotic platform.

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%.

Motivation & Objective

• To address covariate shift in model-free imitation learning for fine manipulation without requiring interactive expert feedback or an environment model.
• To improve policy generalization in low-data, high-precision manipulation tasks using only expert demonstration data.
• To develop a scalable, non-interactive method that enhances data support and corrects error accumulation in policy learning.
• To demonstrate the method on a real-world robotic system equipped with actuated chopsticks for grasping small, slippery objects.
• To achieve human-level performance in fine manipulation tasks using simple tools and minimal expert interaction.

Proposed method

• Transforms state data into an object-centric frame to increase data density around critical grasp states, improving local policy learning.
• Applies noise injection during training by perturbing states with Gaussian noise, generating synthetic corrective labels to improve robustness.
• Uses a combination of parametric (behavior cloning) and non-parametric (k-NN) methods to match action distributions at unseen states.
• Implements a hybrid ensemble policy that switches between behavior cloning and k-NN based on neighbor distance thresholds to balance accuracy and robustness.
• Applies a weighted loss function combining position, rotation, and opening angle errors using mean squared error and rotation difference metrics.
• Employs a custom PID controller and calibration to reduce end-effector error from 10 mm to 4 mm, improving baseline performance.

Experimental results

Research questions

• RQ1 Can covariate shift in model-free imitation learning be effectively mitigated without interactive expert feedback or environment models?
• RQ2 How does noise injection in state space improve policy generalization in fine manipulation tasks?
• RQ3 To what extent can object-centric data transformation enhance data support in critical grasp regions?
• RQ4 Can a combination of parametric and non-parametric methods better match action distributions at unseen states than behavior cloning alone?
• RQ5 Does the proposed method achieve performance comparable to human experts in real-world fine manipulation with chopsticks?

Key findings

• The proposed method increases the success rate of a real robotic system from 37.3% to 80% in grasping small cube- and ball-shaped objects, approaching the human expert performance of 82.6%.
• The agent achieves a 60% success rate on the most challenging object (small ball), compared to only 12% for a naive behavior cloning baseline.
• Ablation studies confirm that both noise injection and data transformation contribute significantly to reducing covariate shift and improving policy robustness.
• Visualization of state distributions shows reduced divergence from the expert’s state distribution when using the proposed methods.
• The noise injection method generalizes well to simulated MuJoCo environments, indicating transferability beyond the real-world testbed.
• The method avoids the need for interactive data collection, reducing expert burden by eliminating the need for real-time correction during demonstrations.