REVIEW 7 cited by
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
read the original abstract
Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given the very limited budget of human intervention, it remains challenging to design when and how human expert interacts with the learning agent in the training. In this work, we develop a novel human-in-the-loop learning method called Human-AI Copilot Optimization (HACO).To allow the agent's sufficient exploration in the risky environments while ensuring the training safety, the human expert can take over the control and demonstrate how to avoid probably dangerous situations or trivial behaviors. The proposed HACO then effectively utilizes the data both from the trial-and-error exploration and human's partial demonstration to train a high-performing agent. HACO extracts proxy state-action values from partial human demonstration and optimizes the agent to improve the proxy values meanwhile reduce the human interventions. The experiments show that HACO achieves a substantially high sample efficiency in the safe driving benchmark. HACO can train agents to drive in unseen traffic scenarios with a handful of human intervention budget and achieve high safety and generalizability, outperforming both reinforcement learning and imitation learning baselines with a large margin. Code and demo videos are available at: https://decisionforce.github.io/HACO/.
Forward citations
Cited by 7 Pith papers
-
WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation
WARP trains a reward model on time-warped successful demonstrations to produce frame-level progress estimates that upweight high-advantage chunks during behavior cloning, maintaining high success rates on suboptimal d...
-
WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation
Self-supervised relative progress from time-warped demos reweights BC action chunks, sustaining ~19/20 success and up to ~18× throughput on mixed-quality T-shirt folding where vanilla BC fails.
-
WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation
Self-supervised relative progress velocities from time-warped demos let WARP-BC reweight action chunks so imitation policies stay robust as suboptimal teleop data is admitted.
-
DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation
DeMaVLA is a VLA foundation model using a pruned action expert and flow matching, pre-trained on 5000 hours of real demonstrations and post-trained on multi-task folding data with human-in-the-loop correction, reporti...
-
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
OHP-RL adds a state-dependent preference gate that uses intermittent human interventions as relative preferences to guide RL policy learning in contact-rich robot manipulation tasks.
-
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
OHP-RL uses a state-dependent preference gate to incorporate intermittent human interventions as guidance for RL policy learning in robot manipulation, yielding higher success rates and lower intervention effort on Fr...
-
Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution
Bounded autonomy using typed action contracts and consumer-side execution lets LLMs safely operate enterprise systems, achieving 23 of 25 tasks with zero unsafe executions versus 17 for unconstrained AI across 25 trials.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.