Sergey Levine

Office & Mailing Address:

Gates Building, Room 286

353 Serra Mall

Stanford, CA 94305

Email:

Curriculum Vitae:

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This paper introduces a guided policy search algorithm that uses trajectory optimization to direct policy learning and avoid poor local optima. Using differential dynamic programming to guide the policy search, this method is able to train general-purpose neural network controllers to execute complex, dynamic behaviors such as running on high-dimensional simulated humanoids.

This paper introduces a new probabilistic inverse optimal control algorithm for learning reward functions in Markov decision processes. The method is suitable for large, continuous domains where even computing a full policy is impractical. By using a local approximation of the reward function, this method can also drop the assumption that the demonstrations are globally optimal, requiring only local optimality. This allows it to learn from examples that are unsuitable for prior methods.

This work presents a method for animating characters performing user-specified tasks by using a probabilistic motion model, which is trained on a small number of artist-provided animation clips. The method uses a low-dimensional space learned from the example motions to continuously control the character's pose to accomplish the desired task. By controlling the character through a reduced space, our method can discover new transitions, precompute a control policy, and avoid low quality poses.

This paper describes a method for generating physically plausible responses for animated characters without requiring their motion to be strictly physical. Given a stream of poses, the method simulates plausible responses to physical disturbances and environmental variations. Since the quasi-physical simulation accounts for the dynamics of the character and surrounding objects without requiring the motion to be physically valid, it is suitable for both realistic and stylized, cartoony motions.

This paper presents an inverse reinforcement learning algorithm for learning unknown nonlinear reward functions. The algorithm uses Gaussian processes and a probabilistic model of the expert to capture complex behaviors from suboptimal stochastic demonstrations, while automatically balancing the simplicity of the learned reward structure against its consistency with the observed actions.

In this article, we present a method for efficiently synthesizing animations for characters traversing complex dynamic environments by sequencing parameterized locomotion controllers using space-time planning. The controllers are created from motion capture data, and the space-time planner determines the optimal sequence of controllers to reach a goal in a dynamic, changing environment.

This paper presents an algorithm for learning an unknown reward function for a Markov decision process when good basis features are not available, using example traces from the MDP's optimal policy. The algorithm constructs reward features from a large collection of component features, by building logical conjunctions of those component features that are relevant to the example policy.

Gesture controllers learn optimal policies to generate smooth, compelling gesture animations from speech and other optional inputs. The accompanying video presents examples of various controllers, including controllers that recognize key words, admit manual manipulation of gesture style, and even animate a character with a non-humanoid morphology.

This paper presents the body language synthesis system described in my undergraduate thesis. The method automatically synthesizes body language animations directly from the participants' speech signals, without the need for additional input. The body language animations are synthesized by selecting segments from motion capture data of real people in conversation in real time.

In this report, I describe a new approach for synthesizing body language from prosody using a set of intermediate motion parameters that can be used to describe stylistic qualities of gesture independent of their form. The quality of synthesized motion parameters is compared to the parameters of the original motions accompanying an utterance to obtain a quantitative measure of the performance of the method.

In my undergraduate thesis, I describe the body language synthesis system. This system generates believable body language animations from live speech input, using only the prosody of the speaker's voice. Since the method is suitable for live speech, it can be used in interactive applications, such as networked virtual worlds.

Rendering the Eagle Nebula

My project for Pat Hanrahan's CS 348B rendering class, completed in collaboration with Edward Luong, received the Grand Prize in the rendering competition. The image is of the Eagle Nebula. The rendering used volumetric photon mapping and simulated the excitement of gases in the nebula by ultraviolet radiation, and the resulting emission of lower-wavelength light.

Airship Combat

My project for Marc Levoy's introductory computer graphics course received the "most creative" award in the final project competition. In this game, players control sail-powered airships armed with cannons. The game simulates the physics of the sails using a simple explicit Euler scheme, and uses hierarchical collision detection to detect hits.

Nali Chronicles

Nali Chronicles is a mod for Unreal Tournament that I developed with a group of friends around 2001-2005. The game contains a complete single-player campaign, with original artwork, scripting, items, enemies, and so forth. My part of the project consisted of organizing the group, programming, 3d modeling, and some level design. The game is a little outdated by now, but if you have a copy of the original Unreal Tournament, you can download the installer and give it a go.