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Found 21 papers, 14 papers with code
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively exploring the expansive state-action space.
A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators.
PWM: Policy Learning with Multi-Task World Models
Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments.
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology.
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
We benchmark its effectiveness on DMC-GB2 - our proposed extension of the popular DMControl Generalization Benchmark - as well as tasks from Meta-World and the Distracting Control Suite, and find that our method, SADA, greatly improves training stability and generalization of RL agents across a diverse set of augmentations.
TD-MPC2: Scalable, Robust World Models for Continuous Control
TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model.
Finetuning Offline World Models in the Real World
In this work, we seek to get the best of both worlds: we consider the problem of pretraining a world model with offline data collected on a real robot, and then finetuning the model on online data collected by planning with the learned model.
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing.
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
To incorporate semantics in 3D, the reconstruction module utilizes a vision-language foundation model ($\textit{e. g.}$, Stable Diffusion) to distill rich semantic information into the deep 3D voxel.
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework.
Deep Reinforcement Learning
Model-based Reinforcement Learning
+2
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks.
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so.
Visual Reinforcement Learning with Self-Supervised 3D Representations
A prominent approach to visual Reinforcement Learning (RL) is to learn an internal state representation using self-supervised methods, which has the potential benefit of improved sample-efficiency and generalization through additional learning signal and inductive biases.
Graph Inverse Reinforcement Learning from Diverse Videos
Research on Inverse Reinforcement Learning (IRL) from third-person videos has shown encouraging results on removing the need for manual reward design for robotic tasks.
Temporal Difference Learning for Model Predictive Control
Data-driven model predictive control has two key advantages over model-free methods: a potential for improved sample efficiency through model learning, and better performance as computational budget for planning increases.
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation
We propose a setting for robotic manipulation in which the agent receives visual feedback from both a third-person camera and an egocentric camera mounted on the robot's wrist.
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead.
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation
Our method greatly improves stability and sample efficiency of ConvNets under augmentation, and achieves generalization results competitive with state-of-the-art methods for image-based RL in environments with unseen visuals.
Generalization in Reinforcement Learning by Soft Data Augmentation
Extensive efforts have been made to improve the generalization ability of Reinforcement Learning (RL) methods via domain randomization and data augmentation.
Self-Supervised Policy Adaptation during Deployment
A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data
In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed.
