New robotics and physical-AI papers, with what each one means for the industry.
Mastering robot manipulation skills via reinforcement learning (RL) remains largely sample-inefficient. The most common RL algorithms rely on random action sampling to discover new strategies, resulting in agents that allocate most of their training budget to motions in free space, away from the contacts from which manipulation skills emerge.
This preprint introduces GroundingPI, a 4B grounding foundation model that predicts object points and boxes as quantized token coordinates rather than using a general-purpose vision-language backbone. Across 34 grounding benchmarks it averages 73.68%, ahead of a larger GPT-6 Astra baseline at 71.54%, and as a visual backbone it improves downstream manipulation (RoboTwin 2.0, RoboCasa-GR1) and autonomous driving (nuScenes L2 0.296 m). The result matters because it argues grounding is a distinct perceptual layer that can make embodied foundation models more precise.
This preprint introduces AC-PAC-NMPC, a hybrid controller that places RL-trained actor, critic, and sensor-prediction models inside a sampling-based stochastic NMPC framework to provide finite-time probabilistic safety bounds and long-horizon vision-based navigation. On a fixed-wing UAV with an onboard depth camera, it raised real-robot success from 40% (both RL actor and best PAC-NMPC baseline) to 80% with the lowest cost; in simulation it reached 90% success versus 85% for the strongest baseline. This matters for agile robots that need learned long-range behavior without sacrificing formal collision-probability guarantees.
Agile flight tasks such as drone racing and pursuit-evasion require strong acceleration and precise turns, but the available thrust changes as the battery discharges and voltage drops under load. Conservative command limits make this variation easier to tolerate, at the cost of unused performance.
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.
RL policies trained in GPU simulation transfer zero-shot to a commodity humanoid on rough terrain.
Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.