---
_id: '7175'
abstract:
- lang: eng
text: Deep reinforcement learning (RL) policies for robotic control typically overfit
to a single hardware configuration. Any change to the kinematic chain breaks the
learned mapping and requires complete retraining. This paper investigates whether
combining self-attention mechanisms with explicit spatial observations can improve
a policy’s adaptability to varying kinematic topologies. We train a single RL
agent to control a robotic manipulator across different degrees of freedom (DOF),
ranging from a restricted 4-DOF mode to a fully redundant 7-DOF configuration.
Instead of fixed-length state vectors, the method processes the active joints
as a variable-length sequence in an attention buffer, enriching each joint’s representation
with its relative spatial routing and geometric Jacobian influence. This structure
allows the agent to dynamically evaluate the physical utility of its available
actuators. The proposed Architecture reaches a success rate of 82.1% across trained
topologies and 66.6% on unseen configurations, outperforming MLP and generic attention
baselines while remaining nearly collisionfree. Finally, we demonstrate successful
sim-to-real transfer by deploying the simulation-trained agent on a physical Franka
Emika Panda manipulator.
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/227375532
- 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/227375533
citation:
alphadin: 'Mayer, Patrick Thomas ;
Rexilius, Jan: Adapting to Variable
Kinematic Configurations: A Causal-Kinematic Attention Approach for Robotic Control.
In:'
ama: 'Mayer PT, Rexilius J. Adapting to Variable Kinematic Configurations: A Causal-Kinematic
Attention Approach for Robotic Control.'
apa: 'Mayer, P. T., & Rexilius, J. (n.d.). Adapting to Variable Kinematic Configurations:
A Causal-Kinematic Attention Approach for Robotic Control. Presented at the 9th
Iberian Robotics Conference (ROBOT) , Barcelona.'
bibtex: '@inproceedings{Mayer_Rexilius, title={Adapting to Variable Kinematic Configurations:
A Causal-Kinematic Attention Approach for Robotic Control}, author={Mayer, Patrick
Thomas and Rexilius, Jan} }'
chicago: 'Mayer, Patrick Thomas, and Jan Rexilius. “Adapting to Variable Kinematic
Configurations: A Causal-Kinematic Attention Approach for Robotic Control,” n.d.'
ieee: 'P. T. Mayer and J. Rexilius, “Adapting to Variable Kinematic Configurations:
A Causal-Kinematic Attention Approach for Robotic Control,” presented at the 9th
Iberian Robotics Conference (ROBOT) , Barcelona.'
mla: 'Mayer, Patrick Thomas, and Jan Rexilius. Adapting to Variable Kinematic
Configurations: A Causal-Kinematic Attention Approach for Robotic Control.'
short: 'P.T. Mayer, J. Rexilius, in: n.d.'
conference:
end_date: 2026-11-20
location: Barcelona
name: '9th Iberian Robotics Conference (ROBOT) '
start_date: 2026-11-18
date_created: 2026-09-21T16:34:09Z
date_updated: 2026-09-21T19:31:42Z
department:
- _id: '102'
keyword:
- Reinforcement Learning
- Robotic Manipulation
- Morphology Generalization
- Sim-to-Real Transfer
language:
- iso: eng
project:
- _id: A827C0AA-C7DA-11E9-B0AE-1F4CB252D58D
name: Institute for Building Intelligence
publication_status: accepted
quality_controlled: '1'
status: public
title: 'Adapting to Variable Kinematic Configurations: A Causal-Kinematic Attention
Approach for Robotic Control'
type: conference
user_id: '244831'
year: '2026'
...
---
_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: 'Mayer, Patrick Thomas ;
Rexilius, Jan: 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., & 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. A Hybrid Control Framework Using
Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer.
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'
...