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Found 8 papers, 2 papers with code
Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation
Contact-rich bimanual manipulation involves precise coordination of two arms to change object states through strategically selected contacts and motions.
NL-SLAM for OC-VLN: Natural Language Grounded SLAM for Object-Centric VLN
We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natural language navigation instructions in a method for performing landmark-based navigation.
Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?
Designing planners and controllers for contact-rich manipulation is extremely challenging as contact violates the smoothness conditions that many gradient-based controller synthesis tools assume.
GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy
Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often limits their ability to generalize to unseen objects and layouts.
Continuously Improving Mobile Manipulation with Autonomous Real-World RL
We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision.
Equivariant Diffusion Policy
Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning.
VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation
Understanding how humans leverage semantic knowledge to navigate unfamiliar environments and decide where to explore next is pivotal for developing robots capable of human-like search behaviors.
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features.
