새로 나온 로봇, 피지컬 AI 논문과, 각 논문이 업계에 갖는 의미.
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on the command interface between the planner and the controller, which must support accurate execution while remaining easy to predict, and ideally allow new behaviors to be composed from prior ones.
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions. However, its expressivity does not, in general, come with formal guarantees that the generated objects satisfy hard constraints or requirements.
Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to maximize the expected reward while keeping the overall action risk bounded. In this way, the safety issues arising in AD can be mitigated through constrained actions.
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.
DEWO는 World-Action Model(WAM)이 배치 후 경험에서 행동 모방과 함께 관찰된 시각적 미래를 학습하도록 만든 프리프린트 방법이다. 다섯 개 DexJoCo 과제에서 세 가지 WAM 구조의 평균 성공률을 높였고, 실제 Wuji, Sharpa 손 플랫폼 3×3 그리드 평가에서 두 차례 배치 학습 후 초기 성공이 있던 셀의 성공률을 51.0%에서 71.7%로 끌어올렸다. 성공과 실패의 시각적 미래를 모두 예측 목표로 쓰는 지속적 세계 모델 학습이 제어 개선에 기여한다는 점을 보였다.
Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where environmental conditions may change significantly over time. Although recent advances in Visual Foundation Models (VFMs) have improved open-vocabulary semantic segmentation, these models can still suffer from domain shift, which can significantly degrade performance if they are not adapted to the current environment.
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or a reward to maximize. Forward-Backward representations successfully produce such spaces, but at the cost of hundreds of GPU-hours for a single robot.
Encore는 몇 개의 시연을 키프레임과 그리퍼 이벤트 같은 증거 패킷으로 압축하고 코딩 에이전트가 정책 프로그램을 시뮬레이션과 실물 양팔 로봇에서 반복 수정하게 하는 프레임워크다. LIBERO-PRO의 60개 변형 셀에서 시연 3개를 쓴 프로그램이 96.3%, 시연 없이 쓴 프로그램이 94.9%의 성공률을 보였고, 실물 로봇 두 과제에서는 각각 9/10을 기록했다. 아직 동료 심사를 거치지 않은 프리프린트다.
이 프리프린트 논문은 로봇이 명령한 동작과 실제 실행한 동작의 잔차를 자기지도 신호로 사용해 VLA 정책을 배치 중 온라인으로 적응시키는 self-compensating VLA를 제안했다. 저자들은 마찰, 백래시, 컴플라이언스, 중력 보상 오차를 결합한 시뮬레이션 벤치마크 RoboStress와 두 대의 실물 Piper 팔에서 평가했고, 두 팔 각각에서 평균 작업 성공률을 30%포인트 이상 끌어올렸다고 밝혔다. 이 연구는 아직 동료 심사를 거치지 않았다.
Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggregate cross-video accuracy conflates failures of local perception with failures to preserve observation identity, establish correspondence, and compose evidence, obscuring whether local video understanding actually transfers.
We propose a QCQP-representable IMU pre-integration factor that enables certifiable estimation with pre-integrated inertial measurements. To the best of our knowledge, this is the first work to directly incorporate IMU pre-integration into certifiable estimation.
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.
Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructions are ambiguous, unsupported by the environment, or semantically inconsistent. This paper presents a Risk-Aware Semantic Grounding framework for trustworthy LLM-based robot planning.
In this preprint, the authors build an onboard perception pipeline that lets a lightweight Explorer robot find minerals in underground mines from free-text prompts and hand compact, oriented 3D inspection targets to a heavier Inspector robot. Field tests in a test mine and an active magnesite mine detected two mineral types without retraining and condensed about 38,000 raw mineral points into a few dozen polygons, roughly two orders of magnitude smaller. This matters because autonomous mining inspection requires no mine-specific labels and low-bandwidth robot-to-robot handover.
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.
Grinding and sanding are fundamental processes in industrial robotic surface finishing. However, physical trials are expensive and consume workpieces, making reproducible experiments difficult.
Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this improvement to scale across tasks, it must make effective use of experience without requiring human demonstration of each correction.
Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference latency that severely limits real-time robotic control. Existing acceleration methods treat an action chunk as a monolithic computational unit, ignoring a crucial physical reality of receding-horizon control: actions are generated jointly but consumed sequentially, resulting in inherently heterogeneous execution urgencies.
Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched demonstrations. A central challenge is deciding what information should be transferred from the video to the robot.
논문은 arXiv 로봇 피드에서 가져옵니다. 요약이 아직 작성되지 않은 논문은 초록의 첫 부분을 보여줍니다.