Safe Planning
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Related Articles from SNS
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
Announce Type: replace Abstract: Safe and feasible trajectory planning is critical for real-world autonomous driving systems. However, existing learning-based planners rely heavily on expert demonstrations, which not only lack explicit safety awareness but also risk inheriting undesirable behaviors such as speeding from suboptimal human driving data. Inspired by the success of large language models, we propose Plan-R1, a two-stage trajectory planning framework that decouples principle...
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling
Announce Type: replace Abstract: Safe and feasible trajectory planning is critical for real-world autonomous driving systems. However, existing learning-based planners rely heavily on expert demonstrations, which not only lack explicit safety awareness but also risk inheriting undesirable behaviors such as speeding from suboptimal human driving data. Inspired by the success of large language models, we propose Plan-R1, a two-stage trajectory planning framework that decouples principle...
Provably Safe Motion Planning Under Unknown Disturbances
Announce Type: replace Abstract: We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or linearizable dynamics evolving in workspace with arbitrary-shaped obstacles subject to state and control constraints. Safety requirements are formulated as chance-constraints.
EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents
Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for identifying hazards and reasoning about action-conditioned risks, leading agents to either miss risky interactions or over-identify risks. To address this, we propose EMBGuard, the first MLLM-based safety guardrail for embodied agents designed to decouple physical risk reasoning from agent policy.
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions
arXiv:2606.09719v1 Announce Type: new Abstract: Autonomous mobile robots operating in tight environments require motion planning frameworks that account for the physical footprint of the robot. Simplifying the geometry to a point or a circle is conservative and discards information needed to successfully and safely traverse narrow passages. This work proposes a safe local motion planning and control method that guarantees that a polytopic robot footprint stays inside a continuously updated...
Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving
arXiv:2606.03296v1 Announce Type: new Abstract: Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of surrounding traffic. A fundamental bottleneck in current systems is the disconnect between highly expressive uncertainty modeling and interpretable, safe motion planning. In this paper, we propose a novel sample-conditioned differentiable planning framework that bridges this gap by explicitly...
SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Announce Type: replace Abstract: Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction is a fundamental and crucial requirement for the safety and admissibility of planned trajectories on various systems. Moreover, existing FM planners do not ensure the dynamical consistency, which potentially renders trajectories inexecutable.
Defence spending plan delay 'has left the UK less safe and undermined its credibility'
British paratroopers exit an American Chinook during training at an undisclosed training ground less than 50 kilometres from the Russian border in Finland where Nato troops are taking part in Exercise Northern Star, led by the Finnish military, with soldiers working alongside coalition allies and training Nato soldiers together to deter aggressive neighbouring states. The large scale ground exercise, comprising of around 4,000 coalition troops include the use of new technologies, including...
How the FBI and law enforcement plan to deploy drones to keep FIFA World Cup games safe
How the FBI and law enforcement plan to deploy drones to keep FIFA World Cup games safe 03:37 UP NEXT Anthropic Warns AI May Soon Be Able to Improve Itself 00:37 Startup aims to launch data centers to space 07:07 Telecommunications companies are adapting to natural disasters by investing in new tech 02:41 Florida Sues Open AI and Sam Altman Alleging Safety Issues 00:38 Video Shows Moment Blue Origin Rocket Explodes on Launchpad 02:44 NASA’s Jared Isaacman shares moon base plans 13:31
Defence investment plan delay and drift has left the UK less safe, say MPs
British paratroopers exit an American Chinook during training at an undisclosed training ground less than 50 kilometres from the Russian border in Finland where Nato troops are taking part in Exercise Northern Star, led by the Finnish military, with soldiers working alongside coalition allies and training Nato soldiers together to deter aggressive neighbouring states. The large scale ground exercise, comprising of around 4,000 coalition troops include the use of new technologies, including...