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。
Recent advances in 3D reconstruction enable the extraction of temporally consistent 3D motion from RGB observations; however, current Vision-Language-Action (VLA) models still predominantly rely on 2D image-text priors. Therefore, they do not accurately represent the fundamental dynamics of the 3D world associated with physical actions.
In embodied manipulation, Vision Language Action (VLA) models can execute atomic skills, but long-horizon manipulation requires recombining these skills without demonstrations for every skill combination. The full instruction route treats skill chaining as long-horizon imitation.
Flow matching is a common generative recipe for robot foundation models. Vision-language-action models (VLAs) generate action chunks, while world-action models (WAMs) additionally generate future visual states.
Vision-Language-Action (VLA) models are typically deployed as isolated single-agent policies. When several such agents share a workspace, they can coordinate implicitly, by observing one another, or explicitly, by exchanging messages, but it remains unclear when explicit communication becomes necessary.
Quantization lowers deployment costs of vision-language-action (VLA) policies but can compromise task completion. Across five VLA models and 4,050 real-world trials, we systematically reveal that quantization-aware training (QAT) generally improves on post-training quantization (PTQ) but leaves substantial gaps to full precision (FP) on long-horizon tasks and under visual and semantic shifts, while its imitation-based objective does not explicitly use rollout success or partial completion.
Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference latency to grow with action sequence length and limiting closed-loop control frequency. We present Goal-Conditioned Vision-Predictor-Action (VPA), which replaces autoregressive decoders with a lightweight latent control loop.
Runtime failure monitors for robot policies are typically evaluated by how accurately they distinguish failed from successful executions. Yet an accurate alarm may arrive too late for recovery, while an unnecessary intervention may disrupt an execution that would otherwise succeed.
Reinforcement Learning (RL) substantially improves planning performance in autonomous-driving Vision-Language-Action (VLA) models. However, existing RL methods rely on scalar trajectory rewards that measure overall quality but fail to identify specific planning errors or provide actionable correction signals.
Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language models (VLMs) and video generative models (VGMs), respectively. Yet VLAs learn without explicit supervision from future world states, while WAMs that condition actions on predicted futures incur extra inference cost and let visual prediction errors propagate into actions.
Vision-language-action (VLA) policies issue sparse targets whose arrival at the robot controller is delayed and irregular, so a reasonable predicted action can still produce abrupt motion near people. Remedies are evaluated on different streams, delays and metrics.
Pre-trained robot policies, such as Vision-Language-Action (VLA) models, provide a strong foundation for generalist robotic manipulation, but adapting them to new tasks with only a few demonstrations remains challenging. Directly fine-tuning a pre-trained policy often achieves strong in-distribution performance, yet it can distort the representations learned during pre-training and weaken the model's generalization ability under out-of-distribution shifts.
Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints. Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed.
Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless learn primarily by predicting action chunks from images, language, and state.
A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables. Yet even a strong generalist like $π_{0.5}$ achieves 0% pickup success in our captured deployment scenes without adaptation, and collecting real demonstrations in every user's environment is prohibitively expensive.
Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and diverse manipulation dynamics.
本論文はarXiv上のプレプリントである。SimpleTouchは、事前学習済みVLAモデルπ0.5に触覚エキスパートを追加し、タスク固有のデモンストレーションのみで接触リッチ操作を学習する手法である。UniVTACの6タスクで平均77.5%を達成し、触覚ポリシー事前学習済みのFTP-1を10.8ポイント上回り、実機4タスクでも平均71.3%でFTP-1を8.8ポイント上回った。
FastOPDは、大規模VLA基盤モデルを軽量な学生モデルへ転移するオンポリシー蒸留フレームワークである。単一のオンポリシー状態で教師速度を一致させ、自己整合性損失で有限区間の遷移へ伝播させることで、少ない推論ステップでも性能を維持する。LIBEROではπ0.5教師の84%の性能を2ステップで保持し、従来の数ステップ蒸留手法を上回った（本論文はプレプリントである）。
MobiAgentは、長距離移動操作向けに内部ループと外部ループを組み合わせたエージェントフレームワークである。本プレプリントでは、BEHAVIOR-1Kでπ0.5-TAを22.5ポイント上回る平均成功率65.0%を記録し、実機Astribot S1でも自己改善により平均成功率が32.5%から57.5%に向上した。スキルの再利用と視覚検証に基づく再計画が、移動操作の長期タスク自動化に有効であることを示した。
本論文は、視覚言語行動モデルの内部表現をスパースオートエンコーダで解析し、敵対的パッチの存在に強く相関する特徴を線形プローブで検知した場合のみ抑制する防御手法を提案した。π0.5とSmolVLAをLIBERO-10の間欠的UADA攻撃で評価し、条件付き介入が成功率を改善する一方、無条件の抑制は清浄時の性能を大きく低下させることを示した。査読済み学会誌への採録は明記されておらず、プレプリントである。
本論文はプレプリントである。積層造形ワークセル内のFAIRINO FR3ロボットにOpenVLA-OFTを適応し、8ステップの並列アクションチャンクで制御するChunkVLA-AMを提案した。実機42回のA-to-B物体移載試験で39回成功し成功率は92.9%となり、軌道予測誤差は平均1.74 mmに低減した。照明輝度85から125の範囲で低誤差を維持し、積層造形の後処理などへ柔軟な自動化の可能性を示した。
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。