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Found 11 papers, 0 papers with code
Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics
We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action sequences that emulate the demonstrator's behaviour.
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models
We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Transformers trained with DINO.
On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
And third, a replay phase, which informs the robot how to interact with the object.
Language Models as Zero-Shot Trajectory Generators
Large Language Models (LLMs) have recently shown promise as high-level planners for robots when given access to a selection of low-level skills.
Towards A Unified Agent with Foundation Models
Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others.
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning
We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training.
Learning Multi-Stage Tasks with One Demonstration via Self-Replay
In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a single human demonstration, without requiring any prior object knowledge.
Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces
In this paper, we study the problem of zero-shot sim-to-real when the task requires both highly precise control with sub-millimetre error tolerance, and wide task space generalisation.
SAFARI: Safe and Active Robot Imitation Learning with Imagination
We empirically demonstrate how this method increases the performance on a set of manipulation tasks with respect to passive Imitation Learning, by gathering more informative demonstrations and by minimizing state-distribution shift at test time.
Regularizing Trajectory Optimization with Denoising Autoencoders
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning.
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable.
