---
res:
  bibo_abstract:
  - 'Tendon-driven robotic fingers are underactuated and strongly nonlinear, and their
    unidirectional, friction-attenuated tendon tension imposes actuation constraints
    that any practical controller must respect. A previous study established that
    a deterministic learning controller (DDPG) can track a grasp trajectory for such
    a finger and compared it against model-based schemes. The present work extends
    that line of investigation by asking a narrower but unresolved question: for this
    same single-actuator, three-link tendon-driven architecture, how does a deterministic
    actor-critic policy (DDPG) compare with a stochastic, maximum-entropy policy (SAC)
    once a tension-limit penalty is built directly into the learning objective? Using
    the previously validated physics-based simulation model, both agents are trained
    under identical conditions with a reward that jointly penalizes tracking error
    and tension-constraint violation, and are evaluated across random, sinusoidal,
    and step disturbances using steady-state error, RMS, ISE, and IAE. The results
    show a consistent and interpretable trade-off: the stochastic SAC policy attains
    lower tracking error and better disturbance rejection, especially under time-varying
    and abrupt loads, at the cost of noisier control and longer training, whereas
    the deterministic DDPG policy converges faster and produces smoother torque and
    tension profiles but adapts less well to coupled disturbances. The study isolates
    the role of the exploration mechanism and the tension penalty in shaping these
    behaviors and offers practical guidance on selecting between deterministic and
    stochastic actor-critic methods for physically constrained, underactuated tendon-driven
    systems.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Kanat
      foaf_name: Suleimenov, Kanat
      foaf_surname: Suleimenov
  - foaf_Person:
      foaf_givenName: Roza
      foaf_name: Beisembekova, Roza
      foaf_surname: Beisembekova
  - foaf_Person:
      foaf_givenName: Beibit
      foaf_name: Abdikenov, Beibit
      foaf_surname: Abdikenov
  - foaf_Person:
      foaf_givenName: Aiman
      foaf_name: Ozhikenova, Aiman
      foaf_surname: Ozhikenova
  - foaf_Person:
      foaf_givenName: Assylbek
      foaf_name: Ozhiken, Assylbek
      foaf_surname: Ozhiken
  - foaf_Person:
      foaf_givenName: Akim
      foaf_name: Kapsalyamov, Akim
      foaf_surname: Kapsalyamov
      foaf_workInfoHomepage: http://www.librecat.org/personId=257451
    orcid: 0000-0003-3700-6214
    orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-3700-6214/work/230936086
  bibo_doi: 10.37943/CVPA1758
  bibo_issue: '3'
  bibo_volume: 27
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2707-9031
  - http://id.crossref.org/issn/2707-904X
  dct_language: eng
  dct_publisher: Astana IT University@
  dct_title: 'DEEP REINFORCEMENT LEARNING CONTROL OF A TENSION-CONSTRAINED TENDON-DRIVEN
    UNDERACTUATED ROBOTIC FINGER: A COMPARATIVE STUDY OF DDPG AND SAC@'
...