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Found 18 papers, 9 papers with code
Vision Language Models are In-Context Value Learners
Instead, GVL poses value estimation as a temporal ordering problem over shuffled video frames; this seemingly more challenging task encourages VLMs to more fully exploit their underlying semantic and temporal grounding capabilities to differentiate frames based on their perceived task progress, consequently producing significantly better value predictions.
Eurekaverse: Environment Curriculum Generation via Large Language Models
We validate Eurekaverse's effectiveness in the domain of quadrupedal parkour learning, in which a quadruped robot must traverse through a variety of obstacle courses.
On-Robot Reinforcement Learning with Goal-Contrastive Rewards
We find that GCR leads to a more-sample efficient RL, enabling model-free RL to solve about twice as many tasks as our baseline reward learning methods.
Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model
Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation.
DrEureka: Language Model Guided Sim-To-Real Transfer
Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale.
Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models
There have recently been large advances both in pre-training visual representations for robotic control and segmenting unknown category objects in general images.
Eureka: Human-Level Reward Design via Coding Large Language Models
The generality of Eureka also enables a new gradient-free in-context learning approach to reinforcement learning from human feedback (RLHF), readily incorporating human inputs to improve the quality and the safety of the generated rewards without model updating.
Universal Visual Decomposer: Long-Horizon Manipulation Made Easy
Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands decomposing the overarching task into several manageable subtasks to facilitate policy learning and generalization to unseen tasks.
LIV: Language-Image Representations and Rewards for Robotic Control
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations.
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the environment to all past experience, but this wastes model capacity on data that is irrelevant for policy improvement.
Model-based Reinforcement Learning
reinforcement-learning
+1
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
Contrary to inferences from prior work, we find that scaling dataset size and diversity does not improve performance universally (but does so on average).
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question.
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression
Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets.
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
We propose State Matching Offline DIstribution Correction Estimation (SMODICE), a novel and versatile regression-based offline imitation learning (IL) algorithm derived via state-occupancy matching.
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning
Further, CAP adaptively tunes this penalty during training using true cost feedback from the environment.
State Relevance for Off-Policy Evaluation
Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions.
Conservative Offline Distributional Reinforcement Learning
We prove that CODAC learns a conservative return distribution -- in particular, for finite MDPs, CODAC converges to an uniform lower bound on the quantiles of the return distribution; our proof relies on a novel analysis of the distributional Bellman operator.
Likelihood-Based Diverse Sampling for Trajectory Forecasting
We propose Likelihood-Based Diverse Sampling (LDS), a method for improving the quality and the diversity of trajectory samples from a pre-trained flow model.
