새로 나온 로봇, 피지컬 AI 논문과, 각 논문이 업계에 갖는 의미.
스웨덴 KTH 연구팀이 배터리 전압을 정책 입력에 포함한 Battery-Aware RL을 제안했다. 이 정책은 Crazyflie Brushless에서 3.84m/s 원 추적 RMSE를 stock-authority RL 대비 49.3% 줄였고, 20랩 레이싱 시간을 106.22초에서 95.24초로 단축했다. 이 논문은 아직 동료 심사를 거치지 않은 프리프린트다.
Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, depend on future events, refer to surrounding agents or landmarks, and remain relevant as driving conditions evolve.
Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground robots. However, conventional coverage methods typically do not translate perceived terrain into platform-specific reachability, while continuous image exchange imposes a high communication cost.
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning.
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. However, collecting diverse, high-quality interaction videos, such as clips that clearly show a person's full body and unoccluded interactions with objects, poses a practical barrier to scaling this approach.
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.
프롬프트 하나로 휴머노이드가 춤을 따라 하거나 자세를 잡거나 보상을 좇게 하는 전신 제어 모델은 로봇마다 학습에 100 GPU시간 넘게 든다. 빈로보틱스(VinRobotics)와 싱가포르국립대(NUS), 독일 다름슈타트공대의 이번 프리프린트는 유니트리 G1용 모델을 부스터 T1, 푸리에 N1, 자체 로봇 M3로 옮기는 데 공용 인코더 1시간 미만과 로봇당 10 GPU시간 미만이면 충분했고, 추종 정확도는 전용 제어기와 거의 같았다고 밝혔다.
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.
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.
논문은 arXiv 로봇 피드에서 가져옵니다. 요약이 아직 작성되지 않은 논문은 초록의 첫 부분을 보여줍니다.