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Grace Liu

dblp:163/3762 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 91% Robot manipulation · 9%
Human-computer interaction and pervasive computing
1 paper
Personal fabrication and tangible interfaces · 87% Haptics and multimodal interaction · 13%

Topics — the 4 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › exploration
directed exploration
0.912025
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals · ICLR 2025
Machine learning › Reinforcement learning
exploration
0.912025
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals · ICLR 2025
Machine learning › Reinforcement learning › hierarchical reinforcement learning › skill learning
skill discovery
0.912025
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals · ICLR 2025
Personal fabrication and tangible interfaces › computational design tools
end-user design tools
0.612022
Reconfigurable Elastic Metamaterials · UIST 2022

Methods — techniques the papers use, named apart from their topics

contrastive reinforcement learning · 0.9mechanical characterization · 0.63d printing · 0.6
YearPublicationVenuePosition
2025 A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals
abstract
In this paper, we present empirical evidence of skills and directed exploration emerging from a simple RL algorithm long before any successful trials are observed. For example, in a manipulation task, the agent is given a single observation of the goal state (see Fig. 1) and learns skills, first for moving its end-effector, then for pushing the block, and finally for picking up and placing the block. These skills emerge before the agent has ever successfully placed the block at the goal location and without the aid of any reward functions, demonstrations, or manually-specified distance metrics. Once the agent has learned to reach the goal state reliably, exploration is reduced. Implementing our method involves a simple modification of prior work and does not require density estimates, ensembles, or any additional hyperparameters. Intuitively, the proposed method seems like it should be terrible at exploration, and we lack a clear theoretical understanding of why it works so effectively, though our experiments provide some hints.
Grace Liu, Michael Tang, Benjamin Eysenbach
ICLR1
2022 Reconfigurable Elastic Metamaterials
abstract
We present a novel design for materials that are reconfigurable by end-users. Conceptually, we propose decomposing such reconfigurable materials into (1) a generic, complex material consisting of engineered microstructures (known as metamaterials) designed to be purchased and (2) a simple configuration geometry that can be fabricated by end-users to fit their individual use cases. Specifically, in this paper we investigate reconfiguring our material’s elasticity, such that it can cover existing objects and thereby augment their material properties. Users can configure their materials by generating the configuration geometry using our interactive editor, 3D printing it using commonly available filaments (e. g., PLA), and pressing it onto the generic material for local coupling. We characterize the mechanical properties of our reconfigurable elastic metamaterial and showcase the material’s applicability as, e.g., augmentation for haptic props in virtual reality, a reconfigurable shoe sole for different activities, or a battleship-like ball game.
Willa Yunqi Yang, Yumeng Zhuang, Luke Andre Darcy, Grace Liu, Alexandra Ion
UIST4