Author name cluster

Minji Kim

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

BRIC: Bridging Kinematic Plans and Physical Control at Test Time

Dohun Lim
Minji Kim
Jaewoon Lim
Sungchan Kim

We propose BRIC, a novel test-time adaptation (TTA) framework that enables long-term human motion generation by resolving execution discrepancies between diffusion-based kinematic motion planners and reinforcement learning-based physics controllers. While diffusion models can generate diverse and expressive motions conditioned on text and scene context, they often produce physically implausible outputs, leading to execution drift during simulation. To address this, BRIC dynamically adapts the physics controller to noisy motion plans at test time, while preserving pre-trained skills via a loss function that mitigates catastrophic forgetting. In addition, BRIC introduces a lightweight test-time guidance mechanism that steers the diffusion model in the signal space without updating its parameters. By combining both adaptation strategies, BRIC ensures consistent and physically plausible long-term executions across diverse environments in an effective and efficient manner. We validate the effectiveness of BRIC on a variety of long-term tasks, including motion composition, obstacle avoidance, and human-scene interaction, achieving state-of-the-art performance across all tasks.

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IROS Conference 2025 Conference Paper

SPLiCE: Single-Point LiDAR and Camera Calibration & Estimation Leveraging Manhattan World

Minji Kim
Jeahn Han
Jungil Ham
Pyojin Kim

We present a novel calibration method between single-point LiDAR and camera sensors utilizing an easy-to-build customized calibration board satisfying the Manhattan world (MW). Previous methods for LiDAR-camera (LC) calibration focus on line and plane correspondences. However, they require dense 3D point clouds from heavy and expensive LiDAR to simplify alignments; otherwise, these approaches fail for extremely sparse LiDAR. Compact, lightweight, and sparse LiDAR and camera sensors are inevitable for micro drones like Crazyflie with a maximum payload of 15 g, but there are no explicit calibration methods for them. To address these issues, we propose a new extrinsic calibration method with a new calibration board, which rotates like a door to capture geometric features and align them with images. Once we find an initial estimate, we refine the relative rotation by minimizing the angle difference between the grid orientation of the checkerboard and the MW axes. We demonstrate the effectiveness of the proposed method through various LC configurations, achieving its capability and high accuracy compared to other state-of-the-art approaches. We release our calibration toolkit, source codes, and how to make the calibration boards for the robotics community: https://SPLiCE-Calib.github.io/.

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AAAI Conference 2025 Conference Paper

Truncated Gaussian Policy for Debiased Continuous Control

Ganghun Lee
Minji Kim
Minsu Lee
Byoung-Tak Zhang

In continuous domains, reinforcement learning policies are often based on Gaussian distributions for their generality. However, the unbounded support of Gaussian policy can cause a bias toward sampling boundary actions in many continuous control tasks that impose action limits due to physical constraints. This "boundary action bias'' can negatively impact training in algorithms like Proximal Policy Optimization. Despite this, it has been overlooked in many existing research and applications. In this paper, we revisit this issue by presenting illustrative explanations and analysis from the sampling point of view. Then, we introduce a truncated Gaussian policy with inherent bounds as a minimal alternative to mitigate the bias. However, we find that the plain truncated Gaussian policy may lay the counter-bias, preferring interior actions: to balance the bias, we ultimately propose a scale-adjusted truncated Gaussian policy, where the distribution scale shrinks if the location is near the boundaries. This property makes boundary actions deterministic more than in plain truncated Gaussian, but still less than in original Gaussian. Extensive empirical studies and comparisons on various continuous control tasks demonstrate that the truncated Gaussian policies significantly reduce the rate of boundary action usage, while scale-adjusted ones successfully balance the bias and counter-bias. It generally outperforms the Gaussian policy and shows competitive results compared to other approaches designed to counteract the bias.

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IJCAI Conference 2022 Conference Paper

Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking

Ilchae Jung
Minji Kim
Eunhyeok Park
Bohyung Han

This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network. The former learns coarse primary information with low cost while the latter conveys residual information for high fidelity to original representations. The proposed parallel architecture is effective to maintain complementary information since fixed-point arithmetic can be utilized in the quantized network and the lightweight model provides precise representations given by a compact channel-pruned network. We incorporate the hybrid representation technique into an online visual tracking task, where deep neural networks need to handle temporal variations of target appearances in real-time. Compared to the state-of-the-art real-time trackers based on conventional deep neural networks, our tracking algorithm demonstrates competitive accuracy on the standard benchmarks with a small fraction of computational cost and memory footprint.

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ICRA Conference 2018 Conference Paper

Stiffness Decomposition and Design Optimization of Under-Actuated Tendon-Driven Robotic Systems

Minji Kim
Junyoung Park
Juhyeok Kim
Myungsin Kim

We present a novel systematic design framework for general under-actuated tendon-driven (UATD) robotic systems to exhibit desired behaviors both during the free motion and the contact task. For this, we propose stiffness decomposition, which enables us to completely decompose the configuration space of the UATD robotic systems into the actuated space (with full actuation via active tendons) and the un-actuated space (with no actuation, only with passive compliance and contact wrench). The behavior in the actuated space is then fully-controllable, thus, the attainment of the desired behaviors, particularly those during the contact task, hinges upon that in the un-actuated space. For this, relying on the stiffness decomposition, we optimize the design parameters (e. g. , tendon routing, pulley radius, passive compliance, etc.) to ensure the deformation in the un-actuated space as directional (e. g. , for adaptive grasping) and minimized (e. g. , pushing with posture maintained) for different contact wrench sets as possible, while also rendering the free motion to be as compliant and backdrivable as possible. The presented framework is then applied to design a UATD robotic finger and experimentally verified with the robot able to mimic the behavior of human index finger both during the free motion and pinch-pushing.

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