FWIK: Multi-Waypoint IK Selection via Lookahead Graph Search
Overview
This research presents Future-Window Inverse Kinematics (FWIK), an online method that selects inverse kinematics solutions by considering a window of future waypoints to avoid poor arm placement for later steps. The approach generates IK candidates, connects them in a layered joint-space graph, and ranks paths using motion-time estimates while filtering out self-collision risks with a learned model. Evaluated on Franka Emika Panda and KUKA LBR iiwa robots in simulation across Pilz, RRT-Connect, CHOMP, and STOMP planners, FWIK achieved 96.8-100% sequence completion rates and reduced planned trajectory durations by 12.8-13.1% compared to STAMPEDE, 22.5-25.6% versus IKLink, and 27.5-37.2% against retry-based TRAC-IK.
Generated from attributed reports · Updated 3 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“t-waypoint IK goals from the remaining paths in ranked order, and the first goal that yields a valid trajectory is selected for execution. We evaluate FWIK in simulation on two 7-DoF manipulators, the Franka Emika Panda and the KUKA LBR iiwa. Each method-planner setting contains five repetitions of 100 sequences with 50 waypoints. Across Pilz, RRT-Connect, CHOMP, and STOMP, FWIK completes 96.8-100% of sequences and g”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalFWIK: Multi-Waypoint IK Selection via Lookahead Graph Search
This research presents Future-Window Inverse Kinematics (FWIK), an online method that selects inverse kinematics solutions by considering a window of future waypoints to avoid poor arm placement for later steps. The approach generates IK candidates, connects them in a layered joint-space graph, and ranks paths using motion-time estimates while filtering out self-collision risks with a learned model. Evaluated on Franka Emika Panda and KUKA LBR iiwa robots in simulation across Pilz, RRT-Connect, CHOMP, and STOMP planners, FWIK achieved 96.8-100% sequence completion rates and reduced planned trajectory durations by 12.8-13.1% compared to STAMPEDE, 22.5-25.6% versus IKLink, and 27.5-37.2% against retry-based TRAC-IK.
Event coverage history
Current attention 3·Peak within the comparable range 3（2026-11-06 22:00 UTC）·Change within the comparable range over 24 hours –
The trend compares participants observed continuously; its scope may be narrower than current attention totals. Hover or click to explore hourly observations, or use the left and right arrow keys.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).