---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Patrick Thomas
      foaf_name: Mayer, Patrick Thomas
      foaf_surname: Mayer
      foaf_workInfoHomepage: http://www.librecat.org/personId=244831
    orcid: 0009-0009-6800-1273
    orcid_put_code_url: https://api.orcid.org/v2.0/0009-0009-6800-1273/work/227375528
  - foaf_Person:
      foaf_givenName: Jan
      foaf_name: Rexilius, Jan
      foaf_surname: Rexilius
      foaf_workInfoHomepage: http://www.librecat.org/personId=245736
    orcid: 0000-0002-4579-214X
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227375529
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_subject:
  - Reinforcement Learning
  - Hybrid Control
  - Sim-to-Real
  - Robotic Manipulation
  - Inverse Kinematics.
  dct_title: A Hybrid Control Framework Using Reinforcement Learning for Dynamic Robotic
    Manipulation and Sim-to-Real Transfer@
...