ロボットはその記述ではない：形態認識ポリシーの表現堅牢性を評価するGaugeBench
The Robot Is Not Its Description: GaugeBench for Representation Robustness in Morphology-Aware Policies
同じロボットでも関節軸の向きや関節角のゼロ点などの記述規約を変えるだけで、形態認識ポリシーの性能が大きく崩壊することを示し、その原因と対策を検証したベンチマーク研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Rahath Malladi, Arshia Sangwan, Rajesh K. Gupta, Tauhidur Rahman
分類: cs.RO, cs.LG
原文アブストラクト
A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero, and the order and names of links and joints. Morphology-aware policies consume interfaces built from these descriptions, yet cross-embodiment evaluation typically changes the robot while keeping those conventions fixed. This leaves a simple question unanswered: does behavior survive when the robot stays fixed but its description changes? GaugeBench isolates this case by rewriting a fixed mechanism under physically equivalent conventions, verifying that its physics and policy interface are preserved, and then evaluating the same policy weights. The result is stark: three MetaMorph policies score 4030.6 on 80 familiar robots, but only 51.6 when those same robots are equivalently re-described, while 98 genuinely held-out robots score 1489.6. A new description can therefore be more damaging than a new robot. Tracing the failure reveals that axis reversal alone reproduces the collapse, joint-angle zero changes are nearly harmless, and reordering lies between them; moreover, changing joint-state and torque coordinates alone is sufficient to cause the failure, while changing description-derived features alone is not. The same phenomenon appears in ModuMorph and an unrelated PyBullet framework. Yet it is not irreversible: exact two-description transport restores the original controller, and training across equivalent axis conventions raises retained return under axis reversal from 3.6% to 80.6%. Together, these results separate mechanism robustness from representation robustness and show that cross-embodiment evaluation should test both.
関連論文
- EmbodiedSmith：シミュレーションにおける再帰的自己改善フライホイールによる身体性データのスケーリングsim2real
- 安全なリアルタイムロボット制御のためのマイクロニューラルポリシーsim2real
- 脚から車輪へ：移動ベースヒューマノイドのための身体性を考慮した人間動作リターゲティングsim2real
- SMART: 大規模合成事前学習によるゼロショットSim-to-Real関節物体マニピュレーションsim2real
- UWB×Crazyflow：空中ロボティクス向け劣化フィードバックの大規模シミュレーションsim2real
- ROS 2上の無線対応ロボットナビゲーションのためのSionna-Isaac Sim閉ループ連成シミュレーションフレームワークのデモsim2real