Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly. Retrieval policies retain access to demonstrations but struggle with mismatch between recorded and live behavior. We introduce Behavior Predictive Control (BPC), which synthesizes policies without end-to-end policy training by combining an action-aware retrieval metric, a Hankel-based action-continuation prior, and a closed-form one-step residual correction. Inspired by behavioral systems theory, BPC predicts future actions by blending stored observation-action data that best reconstructs the recent runtime observation--action history. Across simulated benchmarks and real-robot deployments, BPC is competitive with learned policies such as π0.5 (surpassing it in some cases), while reducing policy fitting from hours to seconds on consumer GPUs and supporting closed-loop control upwards of 75 Hz on a Jetson Orin Nano. The retrieved demonstration windows and their coefficients also provide an intrinsic estimate of task progress. Retaining demonstrations within the deployed policy makes its predictions traceable to supporting trajectories and enables behavior revision through the demonstration bank.
Figures & tables
Policy family
Fit time
Inference
Memory
Pretraining?
Performance
Interpretable?
Learned [ 1 , 13 , 5 ]
Hours
Slow; real-time may need action chunking
High; may need server GPUs
Frequently
Strong
Opaque
Retrieval [ 7 , 8 ]
Minutes
Fast
Low
Pretrained encoder required
Lower
High
BPC (ours)
Seconds
Fast; 75 Hz on Jetson Orin Nano
Low
Optional; raw pixels may suffice
Strong
High
TABLE I : Policy archetype tradeoffs.
Fig. 2 : Online BPC pipeline. (1) A learned metric retrieves K demonstration windows from the live observation–action history z . (2) Regularized reconstruction fits coefficients gi to their past histories and applies them to future action blocks to form the action prior. (3) Random Fourier features of retrieved observations, actions, and observation differences from the query are uniformly pooled. A linear head predicts the correction δu(t) . The action prior is added to the correction to form the outgoing control action.
TABLE II : Tabletop manipulation hardware: qualitative task sequences and successes over number of trials.
TABLE III : Success rates (%) on simulation tabletop manipulation tasks over 300 rollouts per task.
Task
Success rate
Collision rate (c/m)
Demo-set distance (m)
Demos / demo duration (s)
Loop
10/10
0.0000
0.095
3 / 25.2
Figure-eight
8/10
0.0108
0.109
4 / 33.5
TABLE IV : Drone hardware results for Loop and Figure-eight . Demonstration-set distance is the mean 3D distance to the nearest demonstration trajectory. Demo duration is the mean duration per demonstration.
Fig. 3 : Representative hardware trajectory snapshots for Loop (left) and Figure-eight (right). Demonstrations, flown paths, and predicted references are shown alongside the preceding two second history. Colored demonstration segments indicate normalized coefficient magnitudes, ∣gi∣/∑j∣gj∣ .
TABLE V : Success rates (%) on the dexterous manipulation suites with 120 rollouts per task: DexArt [ 37 ] and Adroit [ 38 ] .
Method
Square
Stack
Coffee
Hammer
Mug
Nut
Stack3
Thread
Mean
NHC
76
88
91
98
63
4
23
56
62
NNC
73
10
91
96
31
9
0
50
45
NLR
71
91
94
99
68
9
27
64
65
BPC (Ours)
73
96
96
99
78
13
50
77
73
TABLE VI : Ablations on tabletop manipulation tasks: success rate (%) over 300 trials per task.
Fig. 5 : Computation scaling across different policies (BPC and π0.5 ) across the number of windows in the bank. Left: fitting time and per-call execution latency (log scale); right: peak GPU memory. Solid lines and filled shapes show fitting, while dotted lines and open shapes show test-time values. Squares mark π0.5 on an RTX 4090, and circles represent BPC values on an RTX 4090.
Setting
MimicGen
Adroit
DexArt
Demonstrations per task
1,000
10
100
History Tini
10 (Square: 5)
10
10
Continuation Tf
10
10
10
Actions executed per update
1
1
1
Warmup
10 (Square: 5)
10
10
Maximum episode length
600
200
250
TABLE VII : Simulation data and temporal settings. Episode limits include demonstration-action warmup. Counts and horizons are in simulator control steps.