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Found 19 papers, 5 papers with code
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
A word/phoneme in the speech signal is represented by a segment of speech signal with variable length and unknown boundary, and this segmental structure makes learning the mapping between speech and text challenging, especially without paired data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
On the other hand, representing RL policies using state machines (Inala et al., 2020) can inductively generalize to long-horizon tasks; however, it struggles to scale up to acquire diverse and complex behaviors.
Location-Aware Visual Question Generation with Lightweight Models
This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location.
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set.
Learning to Act from Actionless Videos through Dense Correspondences
In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations.
Hierarchical Neural Program Synthesis
Then, we train a high-level module to comprehend the task specification (e. g., input/output pairs or demonstrations) from long programs and produce a sequence of task embeddings, which are then decoded by the program decoder and composed to yield the synthesized program.
Diffusion Model-Augmented Behavioral Cloning
Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e. g., behavioral cloning, BC) or the joint probability p(s, a).
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing
The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-task reinforcement learning (MTRL).
Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs
Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) that first learns a program embedding space to continuously parameterize diverse programs from a pre-generated program dataset, and then searches for a task-solving program in the learned program embedding space when given a task.
Skill-based Meta-Reinforcement Learning
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible.
Generalizable Imitation Learning from Observation via Inferring Goal Proximity
Task progress is intuitive and readily available task information that can guide an agent closer to the desired goal.
Learning to Synthesize Programs as Interpretable and Generalizable Policies
To alleviate the difficulty of learning to compose programs to induce the desired agent behavior from scratch, we propose to first learn a program embedding space that continuously parameterizes diverse behaviors in an unsupervised manner and then search over the learned program embedding space to yield a program that maximizes the return for a given task.
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity
Humans can effectively learn to estimate how close they are to completing a desired task simply by watching others fulfill the task.
Program Guided Agent
Developing agents that can learn to follow natural language instructions has been an emerging research direction.
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Model-agnostic meta-learners aim to acquire meta-learned parameters from similar tasks to adapt to novel tasks from the same distribution with few gradient updates.
Toward Multimodal Model-Agnostic Meta-Learning
One important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from.
Multi-view to Novel view: Synthesizing Novel Views with Self-Learned Confidence
We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.
Ranked #1 on
Novel View Synthesis
on Synthia Novel View Synthesis
Model-Agnostic Meta-Learning for Multimodal Task Distributions
In this paper, we augment MAML with the capability to identify tasks sampled from a multimodal task distribution and adapt quickly through gradient updates.
Neural Program Synthesis from Diverse Demonstration Videos
To empower machines with this ability, we propose a neural program synthesizer that is able to explicitly synthesize underlying programs from behaviorally diverse and visually complicated demonstration videos.
