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Regret Bounds for Contextual Bandits Under Slate Feedback

More detail forthcoming.

We gratefully acknowledge research support from Amazon, including access to AWS cloud computing services.

Weakly Supervised Multimodal Feature Representation Learning from Video

A diagram of a machine learning model

Learning strong representation of video data is a challenging task involving not only visual, but auditory, linguistic, and temporal data. Learning such representations becomes even more challenging with the added data volume and processing requirements over traditional image-only representation learning. In order to maintain user privacy, and empower highly...

Amazon-Berkeley Objects: A Large-Scale Dataset for 3D Object Understanding

Overview

Collecting large amounts of high-quality 3D annotations (such as voxels or meshes) for individual real-world objects poses a challenge. One way around this problem is to focus only on synthetic, computer-aided design models. This has the advantage that the data is large in scale, but most objects are untextured and there is no guarantee that the object may exist in...

Hierarchical Model-Based Reinforcement Learning with Temporal Abstractions

For long-horizon tasks in the real world, humans accumulate information over time and make inference using stored memory. An abstract transition model of the world at various time resolutions allows reasoning both globally and locally. Model-based reinforcement learning (MBRL) is efficient for learning policies for robotics control tasks. However, current MBRL approaches only predict future trajectories in a limited time span. A major reason is that the model only learns at one time resolution, typically at the environment transition frequency....

Factorized language representations with knowledge and logic

Knowledge and logical reasoning are essential constituents of spoken and written language. Despite the availability of more compact and accessible representations, such as knowledge graphs (KG) and logic forms, modern NLU technologies predominantly encapsulate knowledge and logical reasoning in vector representations along with other linguistic patterns. Despite its successes, this encapsulation erects barriers between NLU and symbolic technologies developed for knowledge representation and reasoning, which are inherently more transparent, robust, and scalable than their...

Task-Specific World Models for Robotic Manipulation

In this project, we look to develop methods that learn world models for agents to solve difficult real-world robotics tasks. Specifically, we focus on various real-world tasks, such as cable manipulation, that require very fine-grained details of a scene to accurately model future predictions. To do this, we will explore models that localize spatial regions of interest in images, and construct patch or region-based selection methods to acquire fine-grained detailed information.

Researchers Wilson Yan, UC Berkeley...

Modeling Interpersonal Multimodal Signals in Social Conversation

With the integration of VR/AR and robotics in society, the need for socially intelligent AI systems has become more compelling, as people seek to build systems that are more responsive to human interactions, or strive to recreate more embodied telepresence. Furthermore, current advances in 3D human pose estimation have reached levels of accuracy that allow us to tap into in-the-wild datasets to extract poses and study human behavior, which previously was only performed on constrained mocap datasets. Coupled with the demand for social AI, the time is ripe for investigating social...

Control of Microrobots with Data and Computationally Efficient Reinforcement Learning

The ideal method for generating a robot controller would be extremely data efficient, free of requirements on domain knowledge, and safe to run. Model-based reinforcement learning (MBRL) has been established as a compelling approach to synthesize controllers even for systems without analytic dynamics models and with high...

Addressing Challenges in Large-scale Distributed AI Systems

Training Neural Network models is becoming increasingly more expensive, requiring scaling to thousands of processes. This problem is becoming more challenging, as the training data is growing exponentially as well, especially in light of recent unsupervised learning methods. This has made it difficult to apply NN models to large scale problems....

Learning to Collaborate with Human Players

In order for agents trained by deep reinforcement learning to help humans in realistic settings, we will need to ensure that the agents are robust. In collaborative scenarios with humans, evaluating robustness to tail failure cases is nontrivial, as adversarial training (and evaluation) is too conservative of...