継続的模倣学習は言語に基づき続けるか？ロボットタスク保持のための言語摂動ベンチマーク
Does Continual Imitation Learning Remain Grounded? A Language-Perturbed Benchmark for Robotic Task Retention
ロボットが継続的に模倣学習する際、言語指示への理解が保たれるかを評価するベンチマークを提案し、タスク性能が高くても言語に基づく行動が損なわれることを示した。
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？
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著者: Siddeshwar Raghavan, Ziqin Yuan, Fengqing Zhu, Byung-Cheol Min
分類: cs.RO
原文アブストラクト
Continual imitation learning evaluates whether a robot can learn new knowledge without forgetting previously learned skills. However, retaining task performance does not ensure the behavior remains grounded in language because policies may rely on scene cues, object associations, or memorized task structure. We introduce a benchmark protocol to study how language-guided behavior changes as robotic policies learn successive tasks. We construct meaning-preserving and meaning-changing instruction variants for the Goal, Spatial, Object, and Long suites of LIBERO. Policy experiments focus on LIBERO-Goal, evaluating Original and Paraphrase instructions after each continual-learning stage. We compare representative continual imitation learning methods under their original assumptions while separating task competence from language sensitivity. The proposed diagnostics complement standard learning and forgetting metrics by measuring semantic robustness, goal adaptation, and language sensitivity. Results show that strong continual-learning performance does not always translate to reliable language grounding, and our diagnostics help determine whether retained skills remain correctly guided by their instructions. Additional materials are available at https://sites.google.com/view/stillgrounded
