16 accepted papers
Large Language Models (LLMs), despite advances in instruction tuning, often fail to follow complex user instructions. Activation steering techniques aim to mitigate this by manipulating model internals, but have a potential risk of oversteering, where excessive emphasis on the instruction degrades t…
Skill discovery methods enable agents to tackle intricate tasks by acquiring diverse and useful skills from task-agnostic datasets in an unsupervised manner. To apply these methods to more general and everyday tasks, the skill set must be scalable. However, current approaches struggle with this scal…
In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (…
Real-to-sim-to-real systems have been studied to overcome the challenges of robot policy learning in the real world by creating a virtual environment that mimics the actual workspace. However, previous studies have limitations, requiring human assistance, such as observing the workspace with a hand-
As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches to LLM personalization such as prompt-based and training-based methods have been actively explored, the development of ef
Skill discovery methods enable agents to tackle intricate tasks by acquiring diverse and useful skills from task-agnostic datasets in an unsupervised manner. To apply these methods to more general and everyday tasks, the skill set must be scalable. However, current approaches struggle with this scal
While powerful, large language models (LLMs) present significant fine-tuning challenges due to their size. Parameter-efficient fine-tuning (PEFT) methods like LoRA provide solutions, yet suffer from critical optimizer inefficiencies; notably basis redundancy in LoRA’s B matrix when using AdamW, whic
In this paper, we present the tidiness score-guided Monte Carlo tree search (TSMCTS), a novel framework designed to address the tabletop tidying up problem using only an RGB-D camera. We address two major problems for tabletop tidying up problem: (1) the lack of public datasets and benchmarks, and (
The bin packing problem (BPP) is a challenging combinatorial optimization problem with a number of practical applications. This paper focuses on online 3D-BPP, where the packer makes immediate decisions for a loading position as items continually arrive. We propose a novel reinforcement learning alg…
Addressing decision-making problems using sequence modeling to predict future trajectories shows promising results in recent years. In this paper, we take a step further to leverage the sequence predictive method in wider areas such as long-term planning, vision-based control, and multi-task decisio…
In this paper, we perform an object rearrangement task for target retrieval in an environment with a confined space and limited observation directions. The agent must create a collision-free path to bring out the target object by relocating the surrounding objects using the prehensile action, i.e.,…
Designing a socially-aware navigation method for crowded environments has become a critical issue in robotics. In order to perform navigation in a crowded environment without causing discomfort to nearby pedestrians, it is necessary to design a global planner that is able to consider both human-robo…
In this paper, we propose a signed distance field (SDF)-based deep Q-learning framework for multi-object re-arrangement. Our method learns to rearrange objects with non-prehensile manipulation, e.g., pushing, in unstructured environments. To reliably estimate Q-values in various scenes, we train the…
In this letter, we consider vision-based control tasks of which the desired goals are given as target images. The problems are often addressed by an autonomous agent which optimizes a trajectory to minimize a manually designed cost function. However, it is challenging to design a suitable cost funct
In the lateral access environment, the robot be-havior should be planned considering surrounding objects and obstacles because object observation directions and approach angles are limited. To safely retrieve a partially occluded target object in these environments, we have to relocate objects using…
Recent advances of actor-critic methods in deep reinforcement learning have enabled performing several continuous control problems. However, existing actor-critic algorithms require a large number of parameters to model policy and value functions where it can lead to overfitting issue and is difficu…