Combining exploration and imitation in contact-rich task learning on an articulated soft robot arm
Learning from demonstration (LfD) has become a popular approach with the emergence of modern transformer-based algorithms. However, the performance of these policies is limited by the quality of the demonstrations. Combining imitation and exploration promises to train policies that perform better and are more reliable. However, this requires a robotic system that can explore safely without damaging itself or the environment, especially in contact-rich tasks during which the robot must exert forc
Record details
Published: 5 August 2026
Source: Frontiers in Robotics and AI
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 5 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)
arXiv red teaming query · 5 August 2026
Recursive Synthesis for Long-Horizon Terminal Tasks
HuggingFace Daily Papers · 4 August 2026
When Memory Lies: An Empirical Study of Spatial Memory Staleness in VLM Agents
HuggingFace Daily Papers · 4 August 2026
OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents
arXiv cs.HC · 4 August 2026
A game theory for foundation models shows new paths to rational cooperation through similarity inference
arXiv · 4 August 2026
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
arXiv cs.AI · 5 August 2026
How to cite this record
ethics.ai (5 August 2026), “Combining exploration and imitation in contact-rich task learning on an articulated soft robot arm,” evidence record 16353, https://ethics.ai/record/16353 (originally published by Frontiers in Robotics and AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.