Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors. We present APT-RL (Action Pretrained Transformer-based Reinforcement Learning), a unified framework that enables multi-skill locomotion to achieve high-speed traversal in complex environments through autonomous skill transitions utiliz
Record details
Published: 15 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 16 July 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.
SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing
arXiv cs.AI · 15 July 2026
UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following
arXiv cs.AI · 15 July 2026
From Language to Navigation Goals: A Vision-Language Approach for Semantic Navigation of Mobile Robots Using RGB-D Perception
arXiv cs.AI · 15 July 2026
DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments
arXiv cs.HC · 15 July 2026
AgentSociety 2: An Integrated Research Environment for Executable Social Science
arXiv cs.CY · 15 July 2026
Object-centric diffusion policies for real-world robotic-arm imitation learning
Frontiers in Robotics and AI · 15 July 2026
How to cite this record
ethics.ai (15 July 2026), “Agile perceptive multi-skill locomotion for quadrupedal robots in the wild,” evidence record 10921, https://ethics.ai/record/10921 (originally published by arXiv cs.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.