Shared autonomy policy fine-tuning and alignment for robotic tasks
This research paper presents a comprehensive shared autonomy framework for human-in-the-loop policy fine-tuning and alignment in robotic tasks. It integrates multi-agent systems with short- and long-horizon fine-tuning algorithms using Bayesian analysis and deep reinforcement learning. Through human user studies involving a laboratory robot, the authors evaluate how interaction channels and fine-tuning strategies impact system performance and user satisfaction.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and roboticists seeking advanced methodologies for human-in-the-loop policy alignment and shared autonomy architecture.
What the document contains
The document addresses the theoretical formulation, architecture design, and algorithmic implementation of shared autonomy for robotic tasks. It details short- and long-horizon fine-tuning strategies, multi-channel human-robot interaction setups, and empirical user studies measuring system performance and user satisfaction.
The text does not provide legal regulatory compliance guidelines, commercial licensing terms, or safety standards for mass-produced consumer robotics.
- A comprehensive end-to-end shared autonomy framework integrates hierarchical planning with multi-agent systems for human-robot interaction.
- Short-horizon fine-tuning leverages Bayesian analysis to adapt the policy in real-time to human intentions.
- Long-horizon fine-tuning updates baseline policies using Proximal Policy Optimization (PPO) based on interaction feedback.
- Human user studies reveal that combining short- and long-horizon fine-tuning significantly improves task performance and user satisfaction.
- Strategic placement of interaction channels for overrides and evaluative feedback bridges the gap between biological and artificial intelligence.
· Abstract · 1. Introduction · 2. Problem formulation · 3. Policy fine-tuning · 4. Shared autonomy implementation setup · 5. Case study application · 6. User study design · 7. Results and discussion · 8. Conclusions and future work
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
4/5Clear responsibility for AI outcomes
In this paper, we present a comprehensive shared autonomy framework for human-in-the-loop policy fine-tuning and alignment.
Abstract
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
5/5Humans remain in control of consequential decisions
The problem of shared autonomy , that is, how to allocate or share decision-making authority between artificial/machine and biological/human intelligence, specifically on a cognitive level, is thus becoming more relevant.
1. Introduction
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-24
Transparency
3/5Openness about how AI systems work and are used
the baseline, pre-trained policy will be fine-tuned during operation, as it interacts with different human and operational settings.
3.2. Long-horizon policy fine-tuning through shared-RL
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-24
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
By laying out careful hypotheses, we employ objective and subjective metrics to measure the effects of shared autonomy design elements on both the system performance and human user satisfaction.
Abstract
screening score · 2026-10-24