@inproceedings{7175,
  abstract     = {Deep reinforcement learning (RL) policies for robotic control are typically trained for a single hardware configuration. When a joint locks, the learned mapping no longer matches the available actuation. This paper investigates whether combining self-attention with explicit kinematic observations can improve a policy’s adaptability to varying active-joint configurations. We train a single RL agent to control a Franka Emika Panda manipulator across different sets of active joints, from a restricted 4-degree-of-freedom (DOF) mode to the fully redundant 7-DOF configuration. Instead of fixed-length state vectors, the method processes the active joints as a variable-length sequence, enriching each joint’s representation with its relative spatial offset and geometric Jacobian influence. The proposed kinematics-informed attention architecture reaches a success rate of 82.1% across trained configurations and 66.6% on unseen configurations, outperforming multilayer perceptron and generic attention baselines while remaining nearly collision-free. Deployed on a physical Panda, the simulation-trained agent reaches 79.0% success on the unseen configurations, closely matching the simulation on the same target schedule (77.0%).},
  author       = {Mayer, Patrick Thomas and Rexilius, Jan},
  booktitle    = {9th Iberian Robotics Conference (ROBOT) },
  keywords     = {Reinforcement Learning, Robotic Manipulation, Kinematic Adaptation, Sim-to-Real Transfer},
  location     = {Barcelona},
  title        = {{Adapting to Variable Active-Joint Configurations via Kinematics-Informed Attention for Robot Arms}},
  year         = {2026},
}

@inproceedings{7174,
  abstract     = {This paper presents a hybrid control architecture for dynamic robotic picking tasks. The framework combines a Deep Reinforcement Learning policy for high-level interception with a dedicated Inverse Kinematics controller for precise terminal grasping, while mitigating precision limitations of monolithic learning-based approaches. The framework utilizes a Proximal Policy Optimization agent to approach moving targets, seamlessly transitioning to an Inverse Kinematics solver that reduces terminal orientational and positional errors while minimizing cumulative control effort. To facilitate deployment on physical hardware, a robust sim-to-real pipeline incorporating system identification, domain randomization, and latency injection is employed. Experimental results on a physical Franka Emika Panda manipulator validate this hybrid architecture. The system achieves an 80% success rate in pick-and-place tasks, compared to 60.8% for unadapted baselines, with no safety-critical violations such as joint limit breaches or collisions observed during testing.},
  author       = {Mayer, Patrick Thomas and Rexilius, Jan},
  booktitle    = {26th International Conference on Control, Automation and Systems (ICCAS)},
  keywords     = {Reinforcement Learning, Hybrid Control, Sim-to-Real, Robotic Manipulation, Inverse Kinematics.},
  location     = {Sapporo},
  title        = {{A Hybrid Control Framework Using Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer}},
  year         = {2026},
}