VLA architectures exhibit architecture-specific failure signatures at the motor-command level, with direction reversal as a universal predictor and velocity monitoring ineffective for continuous models.
Modular safety guardrails are necessary for foundation-model-enabled robots in the real world
8 Pith papers cite this work. Polarity classification is still indexing.
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The paper fine-tunes Qwen3.5-4B as a driving VLA using serialized decision traces from rule-based planners, reporting reduced ADE and miss rate on a simulator benchmark with camera inputs.
DREAM-Chunk uses test-time sampling and latent-world-model rollouts to select robust action chunks from chunking-based VLA policies, improving performance under stochastic dynamics on simulation and hardware tasks.
Explicit geometry-based feasibility supervision added to diffusion VLA training leads to better physical reliability, task success, and faster learning with limited data in manipulation tasks.
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
A structured literature survey of safety mechanisms in long-horizon robotic manipulation organized by intervention timing and strength of supporting evidence.
A literature survey summarizing modeling, state estimation, control methods, applications, and open challenges for legged robots operating in non-inertial environments where the ground moves or accelerates.
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.
citing papers explorer
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How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures
VLA architectures exhibit architecture-specific failure signatures at the motor-command level, with direction reversal as a universal predictor and velocity monitoring ineffective for continuous models.
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Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
The paper fine-tunes Qwen3.5-4B as a driving VLA using serialized decision traces from rule-based planners, reporting reduced ADE and miss rate on a simulator benchmark with camera inputs.
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DREAM-Chunk: Reactive Action Chunking with Latent World Model
DREAM-Chunk uses test-time sampling and latent-world-model rollouts to select robust action chunks from chunking-based VLA policies, improving performance under stochastic dynamics on simulation and hardware tasks.
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Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study
Explicit geometry-based feasibility supervision added to diffusion VLA training leads to better physical reliability, task success, and faster learning with limited data in manipulation tasks.
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Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
A literature review that defines silent physical-action failures in Physical AI and identifies the lack of complete runtime authorization boundaries across surveyed technical streams.
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Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation
A structured literature survey of safety mechanisms in long-horizon robotic manipulation organized by intervention timing and strength of supporting evidence.
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A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
A literature survey summarizing modeling, state estimation, control methods, applications, and open challenges for legged robots operating in non-inertial environments where the ground moves or accelerates.
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Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.