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Found 11 papers, 10 papers with code
Social Attention: Modeling Attention in Human Crowds
In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity.
Path Planning in Dynamic Environments with Adaptive Dimensionality
In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model.
Robotics
Provably Efficient Imitation Learning from Observation Alone
We design a new model-free algorithm for ILFO, Forward Adversarial Imitation Learning (FAIL), which learns a sequence of time-dependent policies by minimizing an Integral Probability Metric between the observation distributions of the expert policy and the learner.
TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions
TRON achieves this by exploiting the structure of the objective to adaptively smooth the cost function, resulting in a sequence of objectives that can be efficiently optimized.
Robotics Systems and Control Systems and Control
Exploration in Action Space
Parameter space exploration methods with black-box optimization have recently been shown to outperform state-of-the-art approaches in continuous control reinforcement learning domains.
Planning and Execution using Inaccurate Models with Provable Guarantees
In this paper, we propose CMAX an approach for interleaving planning and execution.
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Black-box optimizers that explore in parameter space have often been shown to outperform more sophisticated action space exploration methods developed specifically for the reinforcement learning problem.
CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
In this paper we propose CMAX++, an approach that leverages real-world experience to improve the quality of resulting plans over successive repetitions of a robotic task.
Learning Optimal Decision Making for an Industrial Truck Unloading Robot using Minimal Simulator Runs
In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action.
On the Effectiveness of Iterative Learning Control
However, there is little prior theoretical work that explains the effectiveness of ILC even in the presence of large modeling errors, where optimal control methods using the misspecified model (MM) often perform poorly.
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in the learned model, and the objective mismatch between model fitting and policy computation.
