arXiv AI
Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
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arXiv Computer Vision
Sep 18
Feeling Terrain Before Crossing: World Models for Off-Road Navigation
The paper introduces Feel‑WM, an off‑road navigation world model that incorporates proprioceptive data to predict both visual scenes and the robot’s physical sensations such as slip, tilt, and shake. By learning a future proprioceptive state and failure risk from the robot’s own experience, the model can evaluate planned trajectories using a separable score that balances goal similarity with predicted failure risk. Experiments on real and simulated off‑road data show that Feel‑WM outperforms visual‑only models in both open‑loop planning and closed‑loop navigation for wheeled and legged robots, and it successfully guides a Husky robot around rough terrain on mountain trails where an end‑to‑end policy fails.
arXiv AI
Sep 2
CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction
The paper introduces CoLT-Drive, a 3,536-sample counterfactual long‑tail benchmark for evaluating decision‑level driving affordance prediction, which tests whether models can infer how rare objects affect an ego vehicle’s high‑level actions. It also proposes KPA, a knowledge‑preserving adaptation framework that combines structured prompting, expert merging, and a regime‑aware LoRA mixture‑of‑experts module to improve small VLMs on driving tasks. Experiments show KPA achieves 60.8% pair accuracy on CoLT‑Drive, outperforming the Qwen3‑VL‑2B baseline and LoRA SFT while keeping competitive in‑domain performance.
arXiv Machine Learning
Aug 28
Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals
The paper introduces Safe Contrastive Reinforcement Learning (Safe-CRL), a method that corrects bias in contrastive RL caused by failure-terminated Markov decision processes. By applying mass-weighted InfoNCE and a log-survival-mass score, Safe-CRL uses only a one-bit failure signal to improve survival and goal-reaching performance across twelve robot navigation and locomotion tasks. The approach demonstrates complex failure-avoidance behaviors and completes the theoretical foundation of contrastive RL under failure termination.
arXiv AI
4d ago
Predictive Safety Curricula for Robust Legged Locomotion
arXiv:2609.37070v1 Announce Type: cross Abstract: Rare but consequential failures can persist in learned locomotion policies for legged robots even when average task performance is high, in part beca...
arXiv AI
Sep 16
UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner
arXiv:2609.16368v1 Announce Type: cross Abstract: Vision-language models (VLMs) can generate routes directly from aerial imagery for off-road navigation, but their predictions provide no indication o...
arXiv Computer Vision
Aug 26
Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving
arXiv:2603.28029v3 Announce Type: replace Abstract: Criticality metrics such as time-to-collision (TTC) quantify collision urgency but do not distinguish the operational consequences of false-positiv...