Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Masayoshi Tomizuka, Hui Li, ,
| Summary:
Contact-rich manipulation tasks often exhibit a large sim-to-real gap. For
instance, industrial assembly tasks frequently involve tight insertions where
the clearance is less than 0.1 mm and can even be negative when dealing with a
deformable receptacle. This narrow clearance leads to complex contact dynamics
that are difficult to model accurately in simulation, making it challenging to
transfer simulation-learned policies to real-world robots. In this paper, we
propose a novel framework for robustly learning manipulation skills for
real-world tasks using simulated data only. Our framework consists of two main
components: the "Force Planner" and the "Gain Tuner". The Force Planner plans
both the robot motion and desired contact force, while the Gain Tuner
dynamically adjusts the compliance control gains to track the desired contact
force during task execution. The key insight is that by dynamically adjusting
the robot’s compliance control gains during task execution, we can modulate
contact force in the new environment, thereby generating trajectories similar
to those trained in simulation and narrowing the sim-to-real gap. Experimental
results show that our method, trained in simulation on a generic square
peg-and-hole task, can generalize to a variety of real-world insertion tasks
involving narrow and negative clearances, all without requiring any
fine-tuning. Videos are available at https://dynamic-compliance.github.io.
| Search Query: ArXiv Query: search_query=au:”Xiang Zhang”&id_list=&start=0&max_results=3