Confidence-aware motion prediction for real-time collision avoidance
Andrea Bajcsy
Long-term Human Motion Prediction Workshop
ICRA 2019
Work with Sylvia Herbert, Jaime Fisac, David Fridovich-Keil, Steven Wang, Sampada Deglurkar, Claire Tomlin and Anca Dragan
When robots observe behavior that is not well explained by their predictive models, how do
they produce safe but efficient motions?
Connections to reachability analysis
Confidence-aware prediction & planning
Scaling up to multi-robot, multi-human scenarios
Confidence-aware prediction & planning
Scaling up to multi-robot, multi-human scenarios
Connections to reachability analysis
Low probabilityHigh probability
π π’π» π₯π»; π, π½) β ππ½π(π₯π»,π’π»;π)
Noisily-Rational Human Motion Prediction
αΆπ₯π» = ππ»(π₯π» , π’π»)
[Luce, 1959]
[Baker et al., 2007][Ziebart et al., AAAI 2008]
[Ramachandran et al., IJCAI 2007]
[Finn et al., ICML 2016]
[Pfeiffer et al., IROS 2016]
[Herman et al., ICRA 2015]
[Schultz et al., ICRA 2017]
Robust Robot Planning with Human Predictions
π₯π π
αΆπ₯π = ππ (π₯π , π’π )
Robust Robot Planning with Human Predictions
π₯π π
αΆπ₯π = ππ (π₯π , π’π )
αΆπ π = αππ (π π , ππ )
vs.
[Herbert, 2017]
[Mitchell, 2005]
[Lygeros, 2005]
Hamilton-Jacobi Reachability Analysis
Robust Robot Planning with Human Predictions
π₯π π
αΆπ₯π = ππ (π₯π , π’π )
αΆπ π = αππ (π π , ππ )
vs.
[Herbert, 2017]
π π, π = supπ[π’](β)
infπ’(β)
{ supπ‘β[0,π]
πππ π‘(πππ’,π(π‘))}
αΆπ = ππ π₯π , π’π β π( αππ π π , ππ )
[Mitchell, 2005]
[Lygeros, 2005]
[Herbert*, Chen*, Han, Bansal, Fisac, Tomlin. "FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning." CDC, 2017.]
Robust Robot Planning with Human Predictions
π₯π π
Error boundπ π, π = sup
π[π’](β)infπ’(β)
{ supπ‘β[0,π]
πππ π‘(πππ’,π(π‘))}
αΆπ = ππ π₯π , π’π β π( αππ π π , ππ )
[Mitchell, 2005]
[Lygeros, 2005]
Hamilton-Jacobi Reachability Analysis
[Herbert*, Chen*, Han, Bansal, Fisac, Tomlin. "FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning." CDC, 2017.]
Robust Robot Planning with Human Predictions
π₯π π
[Herbert, 2017]
[Fisac, 2018]
π π, π = supπ[π’](β)
infπ’(β)
{ supπ‘β[0,π]
πππ π‘(πππ’,π(π‘))}
αΆπ = ππ π₯π , π’π β π( αππ π π , ππ )
[Mitchell, 2005]
[Lygeros, 2005]
Hamilton-Jacobi Reachability Analysis
π Crash > ππππππ ππππ‘βπππ β
Robust Robot Planning with Human Predictions
π₯π π
[Herbert, 2017]
[Mitchell, 2005]
[Fisac, 2018]
[Lygeros, 2005]
Robust Robot Planning with Human Predictions
[Mitchell, 2008]
π₯π π
[Herbert, 2017]
[Fisac, 2018]
[Lygeros, 2005]
π Crash > ππππππ ππππ‘βπππ β
What if the predictive model is wrong?
Modeled human goal
UnmodeledObstacle
Robot goal
High Confidence Low Confidence
UnmodeledObstacle
UnmodeledGoal
human goal
human goal 1
human goal 2
robot goal
robot goal 1
robot goal 2
π π’π» π₯π»; π, π½) β ππ½π(π₯π»,π’π»;π)
Bayesian Model Confidence
αΆπ₯π» = ππ»(π₯π» , π’π»)
π π’π» π₯π»; π, π½) β ππ½π(π₯π»,π’π»;π)
Bayesian Model Confidence
αΆπ₯π» = ππ»(π₯π» , π’π»)
π½
π π’π» π₯π»; π, π½) β ππ½π(π₯π»,π’π»;π)
Bayesian Model Confidence
αΆπ₯π» = ππ»(π₯π» , π’π»)
π π’π» π₯π»; π, π½) β ππ½π(π₯π»,π’π»;π)
Bayesian Model Confidence
αΆπ₯π» = ππ»(π₯π» , π’π»)
Bayesian Model Confidence
αΆπ₯π» = ππ»(π₯π» , π’π»)
ππ‘ π½ β π π’π»π‘ π₯π»
π‘ ; π, π½)ππ‘β1(π½)
Bayesian Model Confidence
ππ‘ π½ β π π’π»π‘ π₯π»
π‘ ; π, π½)ππ‘β1(π½)
Fixed confidence Bayesian confidenceπ½
Less confident!
More confident!
Confidence-aware prediction Scaling up to multi-robot, multi-human scenarios
Connections to reachability analysis
Robust motion planning
Confidence-aware prediction Scaling up to multi-robot, multi-human scenarios
Connections to reachability analysis
Robust motion planning
Forward Reachable Set
αΆπ₯π» = ππ»(π₯π» , π’π»)
πΉπ π π₯π» , π‘ β {π₯β²: βπ’π»(β), π₯β² = π(π₯π» , π‘, π’π»(β))}
π(π₯π»(0), π‘, π’π»(β))
π₯π»(0)
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
Ξπ‘ β π£π»
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
Ξπ‘ β π£π»
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
π πππ β π£π»
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
π πππ β π£π»
πππππ β« 0
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
π πππ β π£π»
πππππ > 0
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
π πππ β π£π»
πππππ β 0
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
2 π β π£π» 3 π β π£π»π πππ β π£π»
Forward Reachable Set
αΆβπ₯ = π£π»cos(π’π»)αΆβπ¦ = π£π»sin(π’π»)
2 π β π£π» 3 π β π£π»π πππ β π£π»
modeled goal
Scaling up to multi-robot, multi-human scenarios
Connections between predictions and FRS
Confidence-aware prediction
Robust motion planning
Confidently determining subsets of the FRS to avoid
Scaling up to multi-robot, multi-human scenarios
Connections between predictions and FRS
Confidence-aware prediction
Robust motion planning
Determining subsets of the FRS to avoid
1000 x 1000 x 1000 = 1B 1000 x 1000 x 1000 x 1000 = 1T
1000
1000 x 1000 = 1M
1000
1000
[Bajcsy, 2019]
Hardware Demonstration
Hardware Demonstration
Confidence-aware predictions offer promising directions for scaling
Connections between predictions and FRS
Confidence-aware prediction
Robust motion planning
Determining subsets of the FRS to avoid
1000
1000
1000 x 1000 = 1M
1000 x 1000 x 1000 = 1B
1000 x 1000 x 1000 x 1000 = 1T
1000
Confidence-aware predictions offer promising directions for scaling
Connections between predictions and FRS
Confidence-aware prediction
Robust motion planning
Determining subsets of the FRS to avoid
1000
1000
1000 x 1000 = 1M
1000 x 1000 x 1000 = 1B
1000 x 1000 x 1000 x 1000 = 1T
1000
Papers
Herbert*, Chen*, Han, Bansal, Fisac, Tomlin. "FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning." CDC, 2017.
Fisac*, Bajcsy*, Herbert, Fridovich-Keil, Wang, Tomlin, and Dragan. "Probabilistically Safe Robot Planning with Confidence-Based Human Predictions." RSS, 2018.
Fridovich-Keil*, Bajcsy*, Fisac, Herbert, Wang, Dragan, and Tomlin. βConfidence-Aware Motion Prediction for Real-Time Collision Avoidance.β IJRR, 2019
Bajcsy*, Herbert*, Fridovich-Keil, Fisac, Deglurkar, Dragan, and Tomlin, βA Scalable Framework for Real-Time Multi-Robot, Multi-Human Collision Avoidance.β ICRA, 2019.
Multi-robot, multi-human planning: https://github.com/HJReachability/faSTPeople
Code
Fast and safe robot tracking: https://github.com/HJReachability/fastrack
Pedestrian prediction: https://github.com/shwang/pedestrian_prediction
ROS wrapper for pedestrian prediction: https://github.com/abajcsy/crazyflie_human