새로 나온 로봇, 피지컬 AI 논문과, 각 논문이 업계에 갖는 의미.
PL-MPC는 정책 제약형 TD-M(PC)2 백본에서 비평가 지도, MPPI 종단 가치 평가, 플래너-정책 전이를 동시에 바꾼 방법이다. HumanoidBench에서는 balance-hard의 TAR(Total Average Return)가 98±18에서 387±255로, hurdle은 199±13에서 466±200으로 올랐고, KUKA IIWA14 실물 실험에서는 학습 물체 크기와 미학습 크기 모두에서 TD-M(PC)2보다 높은 성공률을 보였다. 아직 동료 심사를 거치지 않은 프리프린트다.
프리프린트로 공개된 이 논문은 쿼드러패드 로봇이 접촉 전 이미지로부터 평면 발 미끄러짐, 평균 수직 지면 반력, 견인 지수, 이동 비용, 접지 하중률 등 5가지 상호작용 지표를 예측하고, 검증 게이트를 적용한 지속 학습으로 새로운 지형에 적응하면서 과거 성능 저하를 줄이는 파이프라인을 제안했다. 하드웨어 순차 실험에서 과거 앵커 NLL 성능 저하를 리플레이 단독 대비 23.1% 줄였으며, 시뮬레이션 내비게이션에서도 안정적으로 동작했다.
이 논문은 프리프린트로, VLA 모델 추론과 로봇 실행 사이 시간 불일치를 줄이기 위해 2단계 비균일 Flow Matching 디노이징과 분산 실시간 실행 프레임워크를 제안했다. π0.5 기준 모델에서 NFE를 10에서 2로 줄여 모델 추론 시간을 61.557 ms에서 21.956 ms로 2.804배 단축했고, 실물 양팔 옷 접기 과제에서 여섯 가지 실시간 실행 방법을 비교해 Legato가 학습 기반, Temporal Smoothing이 비학습 기반 중 가장 우수했다. 이 결과는 모델 추론 효율과 로봇 시스템 타이밍을 함께 최적화해야 함을 보여준다.
이 논문은 RL로 학습한 액터, 크리틱과 센서 예측 모델을 PAC-NMPC에 결합해 미지 환경에서 확률적 안전을 보장하는 Actor-Critic PAC-NMPC를 제안했다. 고정익 UAV 하드웨어 실험에서 성공률 80%로 기존 RL 액터와 PAC-NMPC의 40%를 크게 웃돌았고, 시뮬레이션에서는 90% 성공률을 기록했다. 이는 학습 기반 장기 계획과 샘플링 기반 SNMPC의 충돌 확률 상한 보장을 동시에 얻는 방법이다. arXiv 프리프린트다.
Yinwang Intelligent Technology 연구진이 공개한 프리프린트에서 의미 이해, 시각적 미래 예측, 행동 생성을 한 모델에 묶은 EWAM을 제안했다. EWAM은 행동 토큰이 얕은 층에서는 시각언어 토큰을, 중간 층에서는 예측 미래 프레임을, 깊은 층에서는 행동 토큰을 주로 읽는 층별 전문화를 학습으로 형성했다. RoboTwin clean-to-random 72.1%, LIBERO 평균 98.8%, 다수 실물 로봇 과제에서 기존 VLA와 WAM 하이브리드보다 높은 성공률을 보였고, 인간 egocentric 사전학습은 미학습 로봇 전이 성공률을 40.1%에서 66.9%로 올렸다.
Accurate human motion prediction is crucial for robotic systems operating around people, particularly in complex indoor spaces. In this study, we assess the relative importance of different sources of information in indoor motion prediction with a human motion diffusion model.
Long-horizon robotic tasks are vulnerable to unexpected environmental changes that can render planned actions ineffective or unsafe. To address this, robots must detect such changes as they occur, interpret their impact, and adjust their actions accordingly.
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.
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additional complexity and cost.
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.
Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with missing robot states, limit their value as humanoid training supervision. We present EgoAlign, a data-construction framework that converts these demonstrations into action and state supervision compatible with a general-purpose, continuous whole-body controller, without collecting physical-robot demonstrations.
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure effective policy learning.
Monocular RGB video provides an accessible source of human demonstrations for upper-body robot motion, yet video-driven human-to-robot transfer remains challenging because body and hand motion are recovered at different spatial scales, human and robot kinematics differ substantially, and fine distal motion is difficult to preserve across embodiments. We present a geometry-preserving motion-retargeting framework that integrates unified body--hand reconstruction with morphology-independent geometric transfer.
Contingency games enable agents to anticipate and plan for other agents' hypothetical intents by constructing trajectories with a shared prefix and intent-dependent branches. While contingency games capture intent uncertainty, existing formulations rely on a single branching time, oversimplifying interactions in which different agents' intentions are revealed at different times.
Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlenecked by unreliable execution and the prohibitive computational cost of fine-tuning for specific robot embodiments and tasks. To bridge this gap, we propose WayFinder, an end-to-end, closed-loop hierarchical VLA framework that circumvents the need for fine-tuning by decoupling high-level task reasoning from low-level kinematic control.
Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual planning benchmark that uses VLM-assisted screening, localized multi-view editing, and quality auditing to insert hazards into real nuScenes scenes while preserving their context.
Robot policies increasingly incorporate semantic reasoning and future-world prediction, yet combining these capabilities does not guarantee that local predictions and actions remain aligned with task progress. We introduce CogWAM, a cognition-guided world-action model that establishes an explicit semantic interface between task reasoning and world-action learning through a persistent Semantic State, which stores completed task events and the active subtask.
We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping. Inspired by MASt3R-SLAM, a recent work on monocular SLAM using two-view 3D reconstruction priors, we extend the work to incorporate depth as a prior on the network's prediction, rather than as geometry to fuse.
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recently, general-purpose multimodal agents have shown great potential for zero-shot robotic task solving.
Adaptive Cruise Control (ACC) systems are typically calibrated for an average driver, often resulting in a mismatch between vehicle behavior and individual expectations during time-critical maneuvers such as highway overtaking. When the ACC is perceived as too conservative and inconsistent, drivers intervene through throttle overrides, providing implicit feedback on the system's behavior.
논문은 arXiv 로봇 피드에서 가져옵니다. 요약이 아직 작성되지 않은 논문은 초록의 첫 부분을 보여줍니다.