ロボティクスとフィジカルAIの最新論文と、それぞれが産業にもたらす意味。
Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based approaches impose infrastructure requirements; simulator and world-model evaluators additionally face prediction errors that can compound over the evaluation horizon.
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively. Although these models learn shared physical dynamics and interaction semantics from embodied trajectories, they are typically trained in isolation, which restricts knowledge reuse between prediction and control.
We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions. Given the same initial observation and low-level action sequence, WorldOracle asks whether a predicted rollout reproduces what happens in the real world, including physical consequences and task-state transitions.
Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment-specific annotations.
Action-conditioned world models offer a promising foundation for generalist robotic intelligence by predicting the consequences of actions. However, existing robotic datasets provide limited coverage of the diverse interaction dynamics required for generalizable world modeling.
RoboCoachは、世界モデル内で長いタスクを模擬実行し、最初に失敗する部分作業と対応技能専門家を特定して追加実演を要求する枠組みである。このプレプリントでは、2つのシミュレーション環境と2台の実機で評価し、実機では150件の部分作業実演を追加するだけでFrankaの成功率を13.3%から75.0%に、AgileXを40.0%から83.8%に改善したと報告している。さらに、更新した専門家は未学習の4つの構成タスクで平均35.0%の成功率を示し、均一取得の共有方策ベースラインの0%を上回った。
論文は arXiv のロボティクス分野から取得しています。当社の要約が未作成の場合は、要旨の冒頭を表示します。