Główne pojęcia
Rapid Motor Adaptation (RMA) enables robotic agents to efficiently learn generalizable manipulation skills that can adapt to a wide range of object properties, external disturbances, and environmental variations.
Streszczenie
The paper presents Rapid Motor Adaptation for Robot Manipulator Arms (RMA2), an extension of the RMA framework to enable versatile object manipulation with robot arms. The key contributions are:
- Incorporating category and instance dictionaries as proxies for encoding object geometry, which is crucial for learning policies that can generalize across diverse objects.
- Using a depth convolutional neural network to estimate the privileged information about the environment, including object properties, during the adaptation phase.
- Applying the RMA framework to a broad spectrum of manipulation tasks involving rigid bodies, such as pick-and-place, peg insertion, and faucet/lever turning.
- Formalizing the objectives of the two learning phases of RMA in a unified manner.
- Demonstrating through extensive experiments on the Maniskill2 benchmark that RMA2 outperforms several strong baselines, including state-of-the-art techniques with automatic domain randomization and vision-based policies.
The paper shows that by incorporating these modifications, RMA2 can achieve superior generalization performance and sample efficiency compared to prior methods across diverse manipulation tasks.
Statystyki
The object mass and friction coefficient are randomized during training.
External disturbances in the form of randomly applied forces are applied to the grasped object.
Observation noise is added to the agent's proprioceptive and visual inputs.
Cytaty
"Rapid Motor Adaptation (RMA) offers a promising solution to this challenge. It posits that essential hidden variables influencing an agent's task performance, such as object mass and shape, can be effectively inferred from the agent's action and proprioceptive history."
"We achieve this through several contributions: 1) We propose category and instance dictionaries as a strong proxy for geometry-aware manipulation (Sec. 3.2.1), which is crucial to learn policies that are not transferable across objects, e.g. grasping handles in different positions."
"As far as we are aware, leveraging these modifications, we are the first to apply rapid motor adaptation to general object manipulation tasks with robot arms."