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。
The NIST Assembly Task Board #1 remains a challenging benchmark for learning-based methods in robotic manipulation. Over short horizons, it demands precise, contact-rich control of small, tight-fitting parts.
Autoregressive robot policies rely on action tokenizers to convert continuous action chunks into compact sequences of discrete prediction targets. Existing tokenizers can reconstruct action chunks from discrete codes, but often leave implicit what each individual token contributes to the reconstructed action.
Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus of natural human demonstrations can instead serve as a reusable data interface for different dexterous hands, without collecting task demonstrations on each target robot.
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.
World Action Models (WAMs) inherit powerful priors from pretrained video generation models, but their variational autoencoder (VAE) representations are optimized for visual reconstruction rather than manipulation. External visual representations can improve manipulation, but existing methods couple representation choices with different integration mechanisms, obscuring their individual effects.
本論文はarXivで公開されたプレプリントである。ロボットのデモンストレーションから物体のキーポイント軌道・接触点・終端状態だけを抽出した「エフェクトプログラム」を生成し、それを条件として実行軌道を生成する世界行動モデルPEWAMを提案した。LIBEROの未学習タスクで1回のデモから90エピソード中40を成功させ、既存手法の最大19を上回り、実機Frankaアームでは人間のビデオから40試行中36を成功させた。
本研究はプレプリントである。DynaHarness は意味推論と物理実行を共有実行契約で結びつけ、失敗記録から再利用可能な能力を修正する動的物理ハーネスである。LIBERO-Pro で凍結方策の 17.5% に対し新規初期状態 800 件で 75.2% を達成し、同じ能力ライブラリでも動的実行が 74.0% と一段階再計画の 63.9% を上回った。
Dream4ACTは、異なる関節構成を持つロボットの目標関節状態を固定形状の多視点画像群であるアクションビューとして表現し、映像拡散モデルでRGB観測と統合学習するプレプリントである。RoboTwin 2.0では平均88.98%の成功率、TriWorldBenchでは65.66点を達成し、4種類の実機プラットフォームで同一チェックポイントによる操作を報告した。学習済みのロボット固有アクションデコーダを不要にする点が重要である。
World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. However, their multi-view interfaces typically tile images or concatenate tokens, leaving the geometric relationships among synchronized cameras implicit.
Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators are not always optimal. We propose Blended DAgger (BlenDAgger), an approach for collecting data to train imitation learning policies by using shared control to blend the policy's and demonstrator's actions during interventions.
Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a scalable foundation for visual simulators that predict action outcomes before physical execution.
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow.
While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly represent actions as target positions or poses. Even methods that incorporate force sensing either use it solely as an observation or, when predicting forces as part of the output, rely on a hybrid force controller.
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effects. We study how repeated interaction can improve this capability without updating the base model.
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online identification is therefore essential for reliable manipulation.
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We present FP2, a lightweight downstream interface that equips task-adapted RFMs with explicit force control while preserving their action-generation capability.
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。