# Glen Berseth

I am a PostDoctoral Researcher at the Berkeley Artificial Intelligence Research (BAIR) group working in the Robotic AI & Learning Lab (RAIL) lab with Sergey Levine.

# Publication Articles

#### Interactive Architectural Design with Diverse Solution Exploration

Glen Berseth, Brandon Haworth, Muhammad Usman, Davide Schaumann, Mahyar Khayatkhoei, Mubbasir Turab Kapadia, Petros Faloutsos

In architectural design, architects explore a vast amount of design options to maximize various performance criteria, while adhering to specific constraints. In an effort to assist architects in such a complex endeavour, we propose IDOME, an interactive system for computer-aided design optimization. Our approach balances automation and control by efficiently exploring, analyzing, and filtering space layouts to inform architects' decision-making better. At each design iteration, IDOME provides a set of alternative building layouts which satisfy user-defined constraints and optimality criteria concerning a user-defined space parametrization. When the user selects a design generated by IDOME, the system performs a similar optimization process with the same (or different) parameters and objectives. A user may iterate this exploration process as many times as needed. In this work, we focus on optimizing built environments using architectural metrics by improving the degree of visibility, accessibility, and information gaining for navigating a proposed space. This approach, however, can be extended to support other kinds of analysis as well. We demonstrate the capabilities of IDOME through a series of examples, performance analysis, user studies, and a usability test. The results indicate that IDOME successfully optimizes the proposed designs concerning the chosen metrics and offers a satisfactory experience for users with minimal training.

#### Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks

Glen Berseth, Christopher Pal

Imitation learning, the ability to reproduce some behaviour, is a challenging and vital problem. It is what enables animals with the ability to understand and mimic from observation. Many SoTA methods for imitation accomplish this via additional data that is often not available in the real world. For example, along with an expert's joint positions, the torques used by the expert are available as well. In this work, we describe a learning system that allows an agent to reproduce imitative behaviour of 3D simulated robots from video. This progress will enable us to create robots that can learn behaviour from observing humans, and allow humans to instruct robots in a very natural form of instruction.

#### Feedback Control for Cassie with Deep Reinforcement Learning

Zhaoming Xie, Glen Berseth, Patrick Clary, Jonathan Hurst, Michiel van de Panne

Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gathered during training, but there has been less work on how to best generate the data to learn from. For continuous action domains, the most common method for generating exploratory actions involves sampling from a Gaussian distribution centred around the mean action output by a policy. Although these methods can be quite capable, they do not scale well with the dimensionality of the action space, and can be dangerous to apply on hardware. We consider learning a forward dynamics model to predict the result, $$(x_{t+1})$$, of taking a particular action, $$(u_{t})$$, given a specific observation of the state, $$(x_{t})$$. With this model we perform internal look-ahead predictions of outcomes and seek actions we believe have a reasonable chance of success. This method alters the exploratory action space, thereby increasing learning speed and enables higher quality solutions to difficult problems, such as robotic locomotion and juggling

#### Model-Based Action Exploration for Learning Dynamic Motion Skills

Glen Berseth, Alex Kyriazis, Ivan Zinin, William Choi, Michiel van de Panne

Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gathered during training, but there has been less work on how to best generate the data to learn from. For continuous action domains, the most common method for generating exploratory actions involves sampling from a Gaussian distribution centred around the mean action output by a policy. Although these methods can be quite capable, they do not scale well with the dimensionality of the action space, and can be dangerous to apply on hardware. We consider learning a forward dynamics model to predict the result, $$(x_{t+1})$$, of taking a particular action, $$(u_{t})$$, given a specific observation of the state, $$(x_{t})$$. With this model we perform internal look-ahead predictions of outcomes and seek actions we believe have a reasonable chance of success. This method alters the exploratory action space, thereby increasing learning speed and enables higher quality solutions to difficult problems, such as robotic locomotion and juggling

#### TerrainRL Sim

Glen Berseth, Xue Bin Peng, Michiel van de Panne

We provide 88 challenging simulation environments that range in difficulty. The difficulty in these environments is linked not only to the number of dimensions in the action space but also to the task complexity. Using more complex and accurate simulations will help push the field closer to creating human-level intelligence. Therefore, we are releasing a number of simulation environments that include local egocentric visual perception. These environments include randomly generated terrain which the agent needs to learn to interpret via visual features. The library also provides simple mechanisms to create new environments with different agent morphologies and the option to modify the distribution of generated terrain.