arXiv subjects

Youngjin Hong

Publications and source records attributed to Youngjin Hong.

6 recordsLinked to original sources

arXiv2026

PointCast: One World Model for Rigid, Articulated, and Deformable Object Manipulation

World models are useful for robotic manipulation because robots can predict how actions change the states of objects before executing them. We present PointCast, a point-set world model that spans rigid, articulated, and deformable object manipulation. Its state is a set of 3D points on the object and the end-effector, mesh-free and topology-agnostic. Each point keeps its identity and is supervised on its own trajectory, which teaches the model where every point goes rather than only the shape the points form. Its backbone is a diffusion transformer that denoises a short window of future point positions, conditioned on the points' recent history and the commanded end-effector motion. The backbone's attention alternates between local and global, and cross-attention to the end-effector carries the coupling. This one architecture at 19.8M parameters and one training recipe cover four regimes, rigid objects, cloth, rope, and multi-joint cabinets, with a separate checkpoint trained for each. Trained on randomized simulation and scored against four baselines on the same metric, it is best on three of four regimes and second on rigid. Trained on a real-world robot teleoperation dataset, it has the lowest mean error in four of its six categories, is second in the other two, and improves on the dataset's own model in all six; zero-shot, its simulation checkpoints are best on two of four captures. Frozen inside sampling-based model-predictive control at one network evaluation per window, it plans four simulated tasks over 64 episodes, competitive with or outperforming every baseline on each. Project website at https://pointcast-wm.github.io.

cs.RO↗

arXiv2026

Which Green Technology to Subsidize? Evidence from Electric Vehicles in South Korea

We develop a framework to compare the relative effectiveness of subsidizing alternative emission-reducing technologies. We show that an intermediate technology may reduce emissions more effectively than the cleanest technology if it induces sufficiently greater substitution away from the prevailing high-emission technology. We apply the framework to the South Korean passenger vehicle market using a demand model that incorporates mileage heterogeneity, an important determinant of fuel-type choice. First, reallocating existing subsidies from battery electric vehicles (BEVs), the cleanest technology, to hybrid electric vehicles (HEVs), an intermediate technology, would reduce total greenhouse gas emissions by an additional 47%. Second, for a BEV-focused subsidy policy to outperform an HEV-focused policy, the carbon intensity of electricity generation would need to fall by approximately 45%. Our findings suggest that HEV subsidies remain more effective than BEV subsidies until consumers become sufficiently willing to switch to BEVs or electricity generation becomes sufficiently decarbonized.

econ.GN↗

arXiv2026

LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation

Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (L2A). However, this one-way paradigm often produces policies that execute tasks without deeper contextual understanding, limiting their ability to generalize or explain their behavior. We argue that the complementary skill of mapping actions back to language (A2L) is essential for developing more holistic grounding. An agent capable of both acting and explaining its actions can form richer internal representations and unlock new paradigms for self-supervised learning. We introduce LACY (Language-Action Cycle), a unified framework that learns such bidirectional mappings within a single vision-language model. LACY is jointly trained on three synergistic tasks: generating parameterized actions from language (L2A), explaining observed actions in language (A2L), and verifying semantic consistency between two language descriptions (L2C). This enables a self-improving cycle that autonomously generates and filters new training data through an active augmentation strategy targeting low-confidence cases, thereby improving the model without additional human labels. Experiments on pick-and-place tasks in both simulation and the real world show that LACY improves task success rates by 56.46% on average and yields more robust language-action grounding for robotic manipulation. Project page: https://vla2026.github.io/LACY/

cs.RO↗

arXiv2026

Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models

Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly challenging because they require robots to manipulate DLO with long-horizon planning and reliable skill execution. Successfully completing such tasks demands adapting to their nonlinear dynamics, decomposing abstract routing goals, and generating multi-step plans composed of multiple skills, all of which require accurate high-level reasoning during execution. In this paper, we propose a fully autonomous hierarchical framework for solving challenging DLO routing tasks. Given an implicit or explicit routing goal expressed in language, our framework leverages vision-language models~(VLMs) for in-context high-level reasoning to synthesize feasible plans, which are then executed by low-level skills trained via reinforcement learning. To improve robustness over long horizons, we further introduce a failure recovery mechanism that reorients the DLO into insertion-feasible states. Our approach generalizes to diverse scenes involving object attributes, spatial descriptions, implicit language commands, and \myred{extended 5-clip settings}. It achieves an overall success rate of 92\% across long-horizon routing scenarios. Please refer to our project page: https://icra2026-dloroute.github.io/DLORoute/

cs.RO↗

arXiv2025

An Instrumental Variables Approach to Testing Firm Conduct under a Bertrand-Nash Framework

Understanding firm conduct is crucial for industrial organization and antitrust policy. In this article, we develop a testing procedure based on the Rivers and Vuong non-nested model selection framework. Unlike existing methods that require estimating the demand and supply system, our approach compares the model fit of two first-stage price regressions. Through an extensive Monte Carlo study, we demonstrate that our test performs comparably to, or outperforms, existing methods in detecting collusion across various collusive scenarios. The results are robust to model misspecification, alternative functional forms for instruments, and data limitations. By simplifying the diagnosis of firm behavior, our method offers researchers and regulators an efficient tool for assessing industry conduct under a Bertrand oligopoly framework. Additionally, our approach offers a practical guideline for enhancing the strength of BLP-style instruments in demand estimation: once collusion is detected, researchers are advised to incorporate the product characteristics of colluding partners into own-firm instruments while excluding them from other-firm instruments.

econ.GN↗

arXiv2025

Branding through responsibility: the advertising impact of CSR activities in the Korean instant noodles market

This paper empirically examines the extent to which a favorable view of a firm, shaped by its social contributions, influences consumer choices and firm sales. Using a favorability rating that reflects media exposure of each firm's corporate social responsibility (CSR) activities in the Korean instant noodles market during the 2010s, we find evidence that improvements in the corporate image of Ottogi - one of the country's largest instant noodle producers - positively affected consumer utility for the firm's products. Notably, Ottogi's annual sales of its major brands increased by an average of 23.7 million packages, or 6.7%, as a result of CSR activities and the associated rise in consumer favorability. This effect is comparable in magnitude to that of a nearly 60% increase in advertising spending. Our findings suggest that CSR can foster firm growth by boosting product sales.

econ.GN↗