arXiv:2604.09487v2 Announce Type: replace-cross Abstract: Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modeling and control. So far, these challenges have hindered policy transfer from simulation to real systems. To bridge this gap, we propose a sim-to-real pipeline that learns a neural network model of this complex actuation and leverages established rigid body simulation for the arm dyna
The proliferation of advanced AI techniques allows for novel approaches to address long-standing challenges in robotics, such as the modeling of complex, non-linear actuation systems.
Improving sim-to-real transfer for muscle-actuated robots accelerates development cycles and lowers barriers to deploying more agile and safer robotic systems in diverse applications.
This research provides a methodology to effectively bridge the gap between simulation and reality for a challenging class of robots, potentially enabling wider adoption of muscle-actuated designs.
- · Robotics companies developing advanced manipulators
- · Automation sector
- · AI/ML research institutions
- · Logistics and manufacturing industries
- · Manufacturers reliant on purely rigid-body robotic systems
- · Developers with limited expertise in complex mechanical modeling
More sophisticated and compliant robots become feasible for real-world deployment.
Accelerated development leads to new applications for robots that require human-like dexterity and safety.
The integration of AI-driven simulation insights fosters rapid iteration and innovation across the entire robotics value chain.
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