Search Results for author:
Found 33 papers, 14 papers with code
Generating Human Interaction Motions in Scenes with Text Control
Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets.
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing.
Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation
To generate composite animations from a multi-track timeline, we propose a new test-time denoising method.
Trajeglish: Traffic Modeling as Next-Token Prediction
A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs.
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters.
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.
Robust and Versatile Bipedal Jumping Control through Reinforcement Learning
This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world.
Synthesizing Physical Character-Scene Interactions
These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene.
PADL: Language-Directed Physics-Based Character Control
In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation.
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning
We present a reinforcement learning (RL) framework that enables quadrupedal robots to perform soccer goalkeeping tasks in the real world.
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots
In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task.
ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills.
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.
AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips.
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Developing robust walking controllers for bipedal robots is a challenging endeavor.
Offline Meta-Reinforcement Learning with Advantage Weighting
That is, in offline meta-RL, we meta-train on fixed, pre-collected data from several tasks in order to adapt to a new task with a very small amount (less than 5 trajectories) of data from the new task.
Learning Agile Robotic Locomotion Skills by Imitating Animals
In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.
Reward-Conditioned Policies
By then conditioning the policy on the numerical value of the reward, we can obtain a policy that generalizes to larger returns.
On Learning Symmetric Locomotion
We describe, compare, and evaluate four practical methods for encouraging motion symmetry.
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
Ranked #1 on
OpenAI Gym
on Humanoid-v2
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors.
SFV: Reinforcement Learning of Physical Skills from Videos
In this paper, we propose a method that enables physically simulated characters to learn skills from videos (SFV).
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.
Robotics Systems and Control
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?
The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts the learning difficulty and the resulting performance.
