CoRL 2026 Workshop

Open Problems in Contact-Rich Loco-Manipulation

A half-day workshop focused on how robots decide, sense, and control contact while moving through the world, spanning prehensile and non-prehensile skills.

Austin, Texas, USA November 12, 2026 2:00–6:00 PM Afternoon in-person workshop

Workshop Theme

Recent advances in robot learning, control, perception, and planning have enabled mobile robots to navigate complex environments and interact more capably with the physical world. Legged robots, humanoids, and mobile manipulators now perform tasks that require whole-body motion, forceful interaction, and adaptation under uncertainty.

Many real-world loco-manipulation problems cannot be solved by grasping alone. Robots must combine prehensile skills, such as grasping and carrying, with non-prehensile skills, such as pushing, dragging, pivoting, bracing, or using sustained body-object contact. The contact strategy is directly coupled to locomotion, balance, onboard sensing, planning, and whole-body control.

This workshop brings together researchers in legged locomotion, mobile manipulation, non-prehensile manipulation, whole-body control, contact-aware planning, onboard perception, and sim-to-real transfer to identify shared challenges and promising research directions.

Core Challenges

Contact-mode decision making

Robots must decide when to grasp, push, drag, brace, lean, pin, wedge, pivot, or exploit the environment, and how to transition between those strategies under uncertainty.

Whole-body coordination under contact

Contacts involving a robot's arms, legs, torso, or base—as well as objects and the environment—affect balance, mobility, force regulation, and task success.

Reasoning under uncertainty

Robots must act despite uncertainty in friction, mass, compliance, center of mass, contact location, slip, object geometry, terrain, and other hidden state.

Generalization across embodiments

The field needs principles and representations that transfer across fixed-base arms, mobile manipulators, quadrupeds with arms, humanoids, soft robots, and dexterous hands.

Benchmarks, metrics, and infrastructure

Progress requires shared task families, metrics, simulation assets, and real-world protocols that capture robustness, safety, recoverability, contact efficiency, and generalization.

Invited Speakers

Alan Fern

Alan Fern

Oregon State University

Robot learning, planning, reinforcement learning, and decision making under uncertainty.

Tao Pang

Tao Pang

Robotics and AI Institute

Contact-rich manipulation, planning, control, and robust physical interaction.

Pulkit Agrawal

Pulkit Agrawal

MIT

Robot learning, legged mobility, manipulation, and sim-to-real transfer.

Emo Todorov

Emo Todorov

Roboti LLC

Optimal control, physics simulation, contact dynamics, and robot movement.

Workshop Format

Invited talks

Four invited talks will frame the central open problems and leave time for direct Q&A on contact-mode selection, whole-body coordination, and uncertainty.

Lightning talks and posters

Accepted workshop papers will be introduced through short spotlights and then discussed in an interactive poster session.

Panel and community report

A moderated panel will bring together speakers, authors, and attendees, with key open problems and research directions summarized in a post-workshop report.

Call for Papers

Short workshop papers

We invite 2–4 page workshop papers on contact-rich loco-manipulation, including new results, early-stage ideas, benchmark proposals, position papers, open problems, negative results, and failure analyses.

Prehensile and non-prehensile skills Whole-body control Contact-mode representations Multimodal sensing Sim-to-real transfer

Audience

Researchers across robot learning, locomotion, manipulation, control, perception, planning, and benchmarking are welcome. We especially encourage late-stage PhD students, postdoctoral researchers, and others early in their careers to participate and present their work.