---
res:
  bibo_abstract:
  - 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.@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/227375532
  - 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/227375533
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_subject:
  - Reinforcement Learning
  - Robotic Manipulation
  - Morphology Generalization
  - Sim-to-Real Transfer
  dct_title: 'Adapting to Variable Kinematic Configurations: A Causal-Kinematic Attention
    Approach for Robotic Control@'
...