KlaskTron-IL: A Contact-Rich Adversarial Imitation Learning Benchmark with a Validated Sim-to-Real Bridge
Overview
This paper introduces KlaskTron, an open-hardware platform for adversarial reinforcement learning featuring a learned actuator model that enables zero-shot sim-to-real transfer.
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Reported quantity · actuator model identification range: 0.2 other · Basis not reported
“0.1), and an asymmetric BC-prior x reward-density interaction. Because the policy interface used throughout this paper is the same one the actuator model bridges to hardware, a policy trained on this benchmark can run unchanged on the physical robot, as we demonstrate zero-shot for the strongest BC baseline, so long as its commands respect the actuator model's $\pm 0.2$ m/s identification range.”
Exact source · revision 1Source owner not reported
Reported quantity · failure modes count: 3 other · Basis not reported
“symmetry-augmented timesteps, and synthetic expert trajectories up to 5M steps generated by the zero-shot-validated PPO policy), and diagnostic baselines that expose three characteristic failure modes of standard IL methods under contact-rich adversarial dynamics: BC's data scaling plateau, GAIL's interpretable but weak-gradient discriminator (Spearman $\rho = -1.0$ peg-ball proximity recovery, but a logit span under”
Exact source · revision 1Source owner not reported
Dataset · modalities: human demonstration · Differing source assertions
“arXiv:2611.03091v1 Announce Type: new Abstract: KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a learned actuator model that supports zero-shot sim-to-real transfer of trained policies. Building on this platform, we contribute KlaskTron-IL: a multi-tier imitation learning dataset (80k human demonstration timesteps collected directly on the physical robot, 320k”
Exact source · revision 1Source owner not reported
Dataset · modalities: synthetic expert trajectories · Differing source assertions
“symmetry-augmented timesteps, and synthetic expert trajectories up to 5M steps generated by the zero-shot-validated PPO policy), and diagnostic baselines that expose three characteristic failure modes of standard IL methods under contact-rich adversarial dynamics: BC's data scaling plateau, GAIL's interpretable but weak-gradient discriminator (Spearman $\rho = -1.0$ peg-ball proximity recovery, but a logit span under”
Exact source · revision 1Source owner not reported
Dataset · embodiments: physical robot
“arXiv:2611.03091v1 Announce Type: new Abstract: KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a learned actuator model that supports zero-shot sim-to-real transfer of trained policies. Building on this platform, we contribute KlaskTron-IL: a multi-tier imitation learning dataset (80k human demonstration timesteps collected directly on the physical robot, 320k”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalKlaskTron-IL: A Contact-Rich Adversarial Imitation Learning Benchmark with a Validated Sim-to-Real Bridge
This paper introduces KlaskTron, an open-hardware platform for adversarial reinforcement learning featuring a learned actuator model that enables zero-shot sim-to-real transfer.
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