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Found 18 papers, 1 papers with code
A Generalist Dynamics Model for Control
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way.
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains
We conduct a set of experiments in the RGB-stacking environment, showing that planning with the learned skills and the associated model can enable zero-shot generalization to new tasks, and can further speed up training of policies via reinforcement learning.
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF volume density) and the dynamic objects' geometry and physical properties (assumed known).
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach
Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks.
The Challenges of Exploration for Offline Reinforcement Learning
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour.
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
Evaluating model-based planning and planner amortization for continuous control
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches.
Learning Dynamics Models for Model Predictive Agents
This paper sets out to disambiguate the role of different design choices for learning dynamics models, by comparing their performance to planning with a ground-truth model -- the simulator.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
We highlight its strengths on standard MO benchmark problems and consider case studies in which we recast offline RL and learning from experts as MO problems.
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Local Search for Policy Iteration in Continuous Control
We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.
Motion-Nets: 6D Tracking of Unknown Objects in Unseen Environments using RGB
In this work, we bridge the gap between recent pose estimation and tracking work to develop a powerful method for robots to track objects in their surroundings.
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+2
Prospection: Interpretable Plans From Language By Predicting the Future
High-level human instructions often correspond to behaviors with multiple implicit steps.
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
In this work, we present an approach to deep visuomotor control using structured deep dynamics models.
SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks
We introduce SE3-Nets, which are deep neural networks designed to model and learn rigid body motion from raw point cloud data.
