2023-08-24 マサチューセッツ工科大学(MIT)
MIT researchers developed an AI technique that enables a robot to develop complex plans for manipulating an object using its entire hand, not just the fingertips. This model can generate effective plans in about a minute using a standard laptop. Here, a robot attempts to rotate a bucket 180 degrees.
Image: Courtesy of the researchers
◆これにより、小型の移動型ロボットが大きなロボットアームの代わりに全身や腕を使って物体を操作できる可能性があり、エネルギー消費やコスト削減に寄与する可能性があります。また、宇宙探査ミッションにも応用できるとされています。研究者は、この新しいアプローチが、従来の手法と同等の性能を提供することを実証しました。
＜関連情報＞
- https://news.mit.edu/2023/ai-technique-robots-manipulate-objects-whole-bodies-0824
- https://ieeexplore.ieee.org/document/10225433
準動的接触モデルの局所的平滑化による接触リッチな操作のためのグローバルプランニング Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-Dynamic Contact Models
Tao Pang,H. J. Terry Suh,Lujie Yang,Russ Tedrake
IEEE Transactions on Robotics Published:21 August 2023
DOI:https://doi.org/10.1109/TRO.2023.3300230
Abstract
The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficulties are often attributed to 1) the explosion of contact modes, 2) stiff, nonsmooth contact dynamics and the resulting exploding/discontinuous gradients, and 3) the nonconvexity of the planning problem. The stochastic nature of RL addresses 1) and 2) by effectively sampling and averaging the contact modes. On the other hand, model-based methods have tackled the same challenges by smoothing contact dynamics analytically. Our first contribution is to establish the theoretical equivalence of the two smoothing schemes for simple systems, and provide qualitative and empirical equivalence on several complex examples. In order to further alleviate 2), our second contribution is a convex, differentiable, and quasi-dynamic formulation of contact dynamics, which is amenable to both smoothing schemes, and has proven to be highly effective for contact-rich planning. Our final contribution resolves 3), where we show that classical sampling-based motion planning algorithms can be effective in global planning when contact modes are abstracted via smoothing. Applying our method on several challenging contact-rich manipulation tasks, we demonstrate that efficient model-based motion planning can achieve results comparable to RL, but with dramatically less computation.
