asRoBallet achieves the first hardware deployment of an end-to-end RL policy for a humanoid ballbot by training in a high-fidelity simulation that models discrete roller mechanics and multi-channel friction for zero-shot transfer.
Cross-embodiment robot manipulation skill transfer using latent space alignment.arXiv preprint arXiv:2406.01968
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
RECENT decouples skill semantics from embodiment-specific bindings via code refactoring to let small language models achieve skill grounding performance matching large language model baselines.
SCAR proposes a joint inverse-forward dynamics framework to learn transferable continuous action representations across embodiments from visual data using regularization and adversarial invariance.
NMR uses VAE-based clustered expert physics refinement and a CNN-Transformer to learn dynamics-aware retargeting, eliminating joint jumps and self-collisions on Unitree G1 while accelerating downstream control policies.
citing papers explorer
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asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics
asRoBallet achieves the first hardware deployment of an end-to-end RL policy for a humanoid ballbot by training in a high-fidelity simulation that models discrete roller mechanics and multi-channel friction for zero-shot transfer.
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TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
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Efficient Skill Grounding via Code Refactoring with Small Language Models
RECENT decouples skill semantics from embodiment-specific bindings via code refactoring to let small language models achieve skill grounding performance matching large language model baselines.
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SCAR: Self-Supervised Continuous Action Representation Learning
SCAR proposes a joint inverse-forward dynamics framework to learn transferable continuous action representations across embodiments from visual data using regularization and adversarial invariance.
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Make Tracking Easy: Neural Motion Retargeting for Humanoid Whole-body Control
NMR uses VAE-based clustered expert physics refinement and a CNN-Transformer to learn dynamics-aware retargeting, eliminating joint jumps and self-collisions on Unitree G1 while accelerating downstream control policies.
- Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion