---
_id: '7174'
abstract:
- lang: eng
  text: 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:
- first_name: Patrick Thomas
  full_name: Mayer, Patrick Thomas
  id: '244831'
  last_name: Mayer
  orcid: 0009-0009-6800-1273
  orcid_put_code_url: https://api.orcid.org/v2.0/0009-0009-6800-1273/work/227375528
- first_name: Jan
  full_name: Rexilius, Jan
  id: '245736'
  last_name: Rexilius
  orcid: 0000-0002-4579-214X
  orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-4579-214X/work/227375529
citation:
  alphadin: '<span style="font-variant:small-caps;">Mayer, Patrick Thomas</span> ;
    <span style="font-variant:small-caps;">Rexilius, Jan</span>: A Hybrid Control
    Framework Using Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real
    Transfer. In:'
  ama: Mayer PT, Rexilius J. A Hybrid Control Framework Using Reinforcement Learning
    for Dynamic Robotic Manipulation and Sim-to-Real Transfer.
  apa: Mayer, P. T., &#38; Rexilius, J. (n.d.). A Hybrid Control Framework Using Reinforcement
    Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer. Presented
    at the 26th International Conference on Control, Automation and Systems (ICCAS),
    Sapporo.
  bibtex: '@inproceedings{Mayer_Rexilius, title={A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer},
    author={Mayer, Patrick Thomas and Rexilius, Jan} }'
  chicago: Mayer, Patrick Thomas, and Jan Rexilius. “A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer,”
    n.d.
  ieee: P. T. Mayer and J. Rexilius, “A Hybrid Control Framework Using Reinforcement
    Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer,” presented
    at the 26th International Conference on Control, Automation and Systems (ICCAS),
    Sapporo.
  mla: Mayer, Patrick Thomas, and Jan Rexilius. <i>A Hybrid Control Framework Using
    Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer</i>.
  short: 'P.T. Mayer, J. Rexilius, in: n.d.'
conference:
  end_date: 2026-10-30
  location: Sapporo
  name: 26th International Conference on Control, Automation and Systems (ICCAS)
  start_date: 2026-10-27
date_created: 2026-09-21T16:30:06Z
date_updated: 2026-09-21T19:31:37Z
department:
- _id: '102'
keyword:
- Reinforcement Learning
- Hybrid Control
- Sim-to-Real
- Robotic Manipulation
- Inverse Kinematics.
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
  name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: A Hybrid Control Framework Using Reinforcement Learning for Dynamic Robotic
  Manipulation and Sim-to-Real Transfer
type: conference
user_id: '244831'
year: '2026'
...