Diff-lfd: Contact-aware model-based learning from visual demonstration for robotic manipulation via differentiable physics-based simulation and rendering
Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …
new skills through expert observation, significantly mitigating the need for laborious manual …
Efficient sim-to-real transfer of contact-rich manipulation skills with online admittance residual learning
Learning contact-rich manipulation skills is essential. Such skills require the robots to
interact with the environment with feasible manipulation trajectories and suitable compliance …
interact with the environment with feasible manipulation trajectories and suitable compliance …
Multi-level reasoning for robotic assembly: From sequence inference to contact selection
Automating the assembly of objects from their parts is a complex problem with innumerable
applications in manufacturing, maintenance, and recycling. Unlike existing research, which …
applications in manufacturing, maintenance, and recycling. Unlike existing research, which …
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is
crucial for intelligent robots. In this work, we introduce $\textit {Diff-Transfer} $, a novel …
crucial for intelligent robots. In this work, we introduce $\textit {Diff-Transfer} $, a novel …
Embracing Safe Contacts with Contact-aware Planning and Control
Unlike human beings that can employ the entire surface of their limbs as a means to
establish contact with their environment, robots are typically programmed to interact with …
establish contact with their environment, robots are typically programmed to interact with …