Search Results for author:
Found 36 papers, 9 papers with code
Bayesian Kernel Shaping for Learning Control
In this paper, we focus on nonparametric regression and introduce a Bayesian formulation that, with the help of variational approximations, results in an EM-like algorithm for simultaneous estimation of regression and kernel parameters.
Local Gaussian Regression
Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally.
Incremental Local Gaussian Regression
Locally weighted regression (LWR) was created as a nonparametric method that can approximate a wide range of functions, is computationally efficient, and can learn continually from very large amounts of incrementally collected data.
Probabilistic Depth Image Registration incorporating Nonvisual Information
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking.
Robust Gaussian Filtering using a Pseudo Measurement
The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement.
Depth-Based Object Tracking Using a Robust Gaussian Filter
To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand.
Interactive Perception: Leveraging Action in Perception and Perception in Action
Recent approaches in robotics follow the insight that perception is facilitated by interaction with the environment.
Robotics
Automatic LQR Tuning Based on Gaussian Process Global Optimization
With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data.
A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation
This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized.
Robotics
Path Integral Guided Policy Search
We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization.
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
In practice, the parameters of control policies are often tuned manually.
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications.
Time-Contrastive Networks: Self-Supervised Learning from Video
While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.
Ranked #3 on
Video Alignment
on UPenn Action
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof.
Online Learning of a Memory for Learning Rates
The promise of learning to learn for robotics rests on the hope that by extracting some information about the learning process itself we can speed up subsequent similar learning tasks.
On the Design of LQR Kernels for Efficient Controller Learning
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.
Combining Learned and Analytical Models for Predicting Action Effects from Sensory Data
In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds.
Probabilistic Recurrent State-Space Models
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
Scaling simulation-to-real transfer by learning composable robot skills
In particular, we first use simulation to jointly learn a policy for a set of low-level skills, and a "skill embedding" parameterization which can be used to compose them.
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot.
A New Age of Computing and the Brain
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC) but can struggle in the context of insertion.
Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks
In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback.
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications.
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation
In this work, we extend hindsight relabelling mechanisms to guide exploration along task-specific distributions implied by a small set of successful demonstrations.
Deformable One-Dimensional Object Detection for Routing and Manipulation
To the best of our knowledge, the topic of detection methods that can extract those initial conditions in non-trivial situations has hardly been addressed.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation
On the other hand, algorithms that entirely separate the spatial representation of the deformable object from the routing and manipulation, often using a representation approach independent of planning, result in slow planning in high dimensional space.
Detection and Physical Interaction with Deformable Linear Objects
On the other hand, while physical interaction with these objects has been done with ground manipulators, there have not been any studies on physical interaction and manipulation of the deformable linear object with aerial robots.
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
Multi-Stage Cable Routing through Hierarchical Imitation Learning
In such settings, learning individual primitives for each stage that succeed with a high enough rate to perform a complete temporally extended task is impractical: if each stage must be completed successfully and has a non-negligible probability of failure, the likelihood of successful completion of the entire task becomes negligible.
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods.
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Large Language Models (LLMs) have been successful at generating robot policy code, but so far these results have been limited to high-level tasks that do not require precise movement.
