触覚制御方策のゼロショットsim-to-real転移によるアグレッシブな振り上げ操作
Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation
視覚型触覚センサのみを頼りに、物体の物理特性を事前知識なしで多様な棒を180度まで振り上げる動的マニピュレーションを実現し、シミュレータで学習した方策を実機へゼロショット転移させた研究。
著者: Thomas Bi, Carmelo Sferrazza, Raffaello D'Andrea
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
This paper aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is presented that is able to swing up poles of different masses, radii and lengths, to an angle of 180 degrees, while relying solely on the feedback provided by the tactile sensor. This is achieved by developing a novel simulator that accurately models the interaction of a pole with the soft sensor. A feedback policy that is conditioned on a sensory observation history, and which has no prior knowledge of the physical features of the pole, is then learned in the aforementioned simulation. When evaluated on the physical system, the policy is able to swing up a wide range of poles that differ significantly in their physical attributes without further adaptation. To the authors' knowledge, this is the first work where a feedback policy from high-dimensional tactile observations is used to control the swing-up manipulation of poles in closed-loop.
関連論文
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- TactSpace: 触覚のシミュレーションから実世界への転移のための物理強化共有潜在空間の学習触覚/sim2real
- HydroShear: 触覚シミュレーションから実機への強化学習のためのハイドロエラスティックせん断シミュレーション触覚/sim2real
- 触覚シミュレータを実写類似・ゼロショット性能で拡張するSightGAN触覚/sim2real
- Tactile Gym 2.0:低コスト高解像度触覚センサを比較するためのSim-to-Real深層強化学習触覚/sim2real
- 深層テクスチャ生成ネットワークによる触覚Sim2Realギャップの削減触覚/sim2real
