KalariSena

A dynamic, balance-challenging Kalaripayattu motion dataset and fall-resilient humanoid tracking framework. This research focuses on the intersection of ancient martial arts kinetics and high-dimensional humanoid robotics, introducing a novel benchmark for extreme postural transitions.

Technical Abstract

Research Phase: V1.8 Analysis

Live Kinetic Validation
Dynamic Stability

94.2%

Success Rate in High-G Rotations

Unique Samples

12k+

Annotated Martial Motion Paths

Recovery Rate

0.042

MSE Post-Collision Restoration

Dataset Volume

4.2TB

Raw 4K Sync-Multi-View Stream

01. RAW INPUT DATA
Kalaripayattu master performing high kick motion

Scenario: Dynamic Kick Recovery

Ancient martial artist in low defensive stance

Scenario: Low-Posture Balance

Traditional Indian martial arts weapon forms

Scenario: Weapon Form Dynamics

02. PROCESSED RESULTS
> pose_stream: cam_alpha_01
> keypoints_detected: 124
> joint_confidence: 0.96
> error_compensation: active
> stability_score: high

Task: Skeleton Extraction

Multi-view motion capture is fused into stable skeletal tracks for fast retargeting.

> retarget_mode: humanoid_v3
> center_of_mass: corrected
> contact_solver: enabled
> fall_event: blocked
> output_state: recovered

Task: Humanoid Retargeting

The pipeline projects martial trajectories onto humanoid rigs with resilient postural correction.

> force_tensor: computed
> support_polygon: tracked
> impact_channel: attenuated
> restore_time: 42ms
> mse: 0.042

Task: Force Distribution Map

Dynamics-aware force modeling helps agents recover from abrupt perturbations without collapse.

Analysis 01


0.042 OPT
Tracking Error (MSE)

Training converges to low error under aggressive motion transitions, enabling stable real-time recovery.

Analysis 02


Control
Sena-v1
Current
Stability Metric

KalariSena achieves stronger balance retention compared to previous baselines across perturbation benchmarks.